health care technology and innovations in medicine essay

• Open access
• Published: 03 January 2022

How innovation can be defined, evaluated and rewarded in health technology assessment

• Juan Carlos Rejon-Parrilla   ORCID: orcid.org/0000-0002-0680-7353 1 ,
• Jaime Espin   ORCID: orcid.org/0000-0001-7299-6554 2 , 3 , 4 &
• David Epstein   ORCID: orcid.org/0000-0002-2275-0916 5  

Health Economics Review volume  12 , Article number:  1 ( 2022 ) Cite this article

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What constitutes innovation in health technologies can be defined and measured in a number of ways and it has been widely researched and published about. However, while many countries mention it as a criterion for pricing or reimbursement of health technologies, countries differ widely in how they define and operationalise it.

We performed a literature review, using a snowballing search. In this paper, we explore how innovation has been defined in the literature in relation to health technology assessment. We also describe how a selection of countries (England, France, Italy, Spain and Japan) take account of innovation in their health technology assessment frameworks and explore the key methodologies that can capture it as a dimension of value in a new health technology. We propose a way of coming to, and incorporating into health technology assessment systems, a definition of innovation for health technologies that is independent of other dimensions of value that they already account for in their systems, such as clinical benefit. We use Spain as an illustrative example of how innovation might be operationalised as a criterion for decision making in health technology assessment.

The countries analysed here can be divided into 2 groups with respect to how they define innovation. France, Japan and Italy use features such as severity, unmet need and therapeutic added value as indicators of the degree of innovation of a health technology, while England, Spain consider the degree of innovation as a separate and additional criterion from others. In the case of Spain, a notion of innovation might be constructed around concepts of `step-change’, `convenience’, `strength of evidence base’ and `impact on future research & development’.

Conclusions

If innovation is to be used as operational criteria for adoption, pricing and reimbursement of health technologies, the concept must be clearly defined, and it ought to be independent from other value dimensions already captured in their health technology assessment systems.

Background and introduction

There is a huge industry dedicated exclusively to the discovery and development of new and innovative health technologies. The average research and development (R&D) investment per approved new compound is about UD$1,5 billion [ 1 , 2 ]. In such a competitive industrial environment, it becomes vital to the industry to read any signals public payers may send around what they value and what they do not regard as relevant when it comes to deciding which health technologies to fund and at what price. Health Technology Assessment (HTA) is defined by the World Health Organization (WHO) as the approach used to inform policy and decision-making in health care, especially on how best to allocate limited funds to health interventions and technologies [ 3 ]. The criteria used to judge what constitutes desirable health interventions and technologies can vary amongst HTA systems depending on their aims and the methodologies picked to reach them.

This paper considers how innovation is defined, evaluated and rewarded in HTA. The term is widely used and encompasses multiple attributes. Most HTA systems evaluate features of innovation that consider the impact of a product from the perspective of current patients (therapeutic benefit, unmet need, safety, administration) or current budget holders (cost), also called the “static” perspective [ 4 ]. Examples of this approach can be seen in the paper published by de Solà-Morales et al. [ 5 ], which looks at how innovation is defined from a current payer’s perspective, or also in the work led by Karl Claxton on the cost-effectiveness threshold that defines the opportunity cost of decisions on new technology in terms of the marginal health displaced in the current NHS [ 6 ]. HTA systems less frequently explicitly consider the “dynamic” consequences or incentives created by a decision to adopt or not a new technology on the direction of future R&D and ultimately, further innovations. These terms overlap to some extent with the idea of the source of innovation being `pulled by demand’ or `pushed by supply’ or entrepreneurship [ 7 ].

Previous reviews in this topic have explored specific aspects of innovation: from an organizational point of view [ 8 ], for medicines [ 5 , 9 , 10 , 11 ] and for medical devices [ 7 ]. However, none look at the question in a holistic way to consider how innovation should be included as a criterion for HTA in practice.

Hence, the overall aim of this article is to construct a broad concept of innovation and a process of tailoring it to individual HTA systems that can be useful for healthcare policy makers considering if and how their HTA frameworks capture innovation. To fulfill this aim, we followed three objectives: First, to assess with reference to the literature the theoretical justification for which attributes of innovation ought to be considered in HTA. Second, to assess how HTA bodies in France, Italy, England, Spain and Japan consider these issues in their assessments for adoption or pricing & reimbursement (P&R). Finally, Spain is taken as a case-study to consider how the degree of innovation should and can be strengthened in HTA decisions, and we discuss the relevance of the findings for other HTA systems.

We performed a literature review, using a snowballing search [ 12 ]. We chose this technique because the literature suggests it is a more effective approach for complex and heterogeneous evidence than more formal protocol-driven searches [ 13 ]. The steps in a snowball search are: 1) Establish the research question and inclusion and exclusion criteria 2) Identify the start set: a small number of seminal papers or highly cited papers 3) Backward snowballing: Reviewing the reference lists of the seminal papers 4) Forward snowballing: Searching for papers that cite the seminal papers.

Our inclusion criteria were that the papers included dealt with the concept of innovation in HTA decisions (adoption, reimbursement or pricing) about all types of health technologies (medicines, devices and diagnostics). We excluded: 1) papers where “innovation” was used as a term to refer exclusively to therapeutic benefit or similar terms, already separately accounted for in HTA; 2) papers that did not add anything new on top of the seminal papers; 3) papers that focused on concepts of organizational innovation that are not relevant to HTA adoption or P&R decisions; 4) editorials; 5) regulatory approval criteria and literature that focus exclusively on efficacy, safety and quality. We included papers both in English and Spanish. There was no limitation on the dates when papers were published. One of the authors of this paper made a first selection of included and excluded papers, a second author double checked it and a third author was available to resolve any discrepancies. The search strategy is described in more detail in Additional file 1 .

Not all concepts are eligible or useful for decision making. Diaby and Goeree [ 14 ] recommended that items need to exhibit all the following properties: ‘value relevance’, ‘understandability’, ‘measurability’, ‘non-redundancy’, ‘independence’ and ‘comprehensiveness’. We use this framework as a test for each feature of innovation identified in the literature, seeking to trim these down to a smaller set of items that jointly display these properties, and could potentially be used as criteria in HTA. We then consider methods that could be used to measure or rank health technologies in practice on the basis of the degree of innovation in the chosen countries. In the end, countries choose the criteria that they feel best align and promote their specific aims. Our intention is to identify those criteria that have some theoretical justification and can be measured.

We also assess how HTA bodies in France, Italy, England, Japan and Spain consider innovation in their assessments for adoption, P&R processes. Our choice of countries is based on our judgment of HTA systems that take different stands on whether and how they account for degree of innovation as an independent source of value of new health technologies. We chose a set of countries that allow us to analyse different approaches to HTA to show how innovation can be embedded in different HTA systems for the evaluation and reimbursement of health technologies. Our reasons for including France and England are that they have internationally leading nationally centralized systems that work following high standards of transparency, one rewarding innovativeness as an independent feature (England) whilst the other entangles the concept more with other criteria (France). Japan presents a recently reformed centrally coordinated HTA system, different to the rest of the countries we will be looking into, in that they reward innovative new technologies by applying a system whereby the technologies considered to be innovative receive a premium price beyond the price of the comparator. Italy, whilst having a national agency, is a more fragmented model, with the added interest of having recently introduced a new method to capture innovation [ 15 ]. Spain goes one step further in how decentralized it is in its’ HTA activities, having several regional agencies as well as national entities, each with parallel competencies. The main interest in this country is that the law includes degree of innovation amongst the criteria that should be used to make P&R decisions for drugs [ 16 ], but provides no guidance on how to define or measure this concept. Despite the size of R&D investment having been consistently higher in the US compared with Europe, and the US being the biggest pole of clinical trials worldwide [ 1 ], we decided not to include the US because P&R decisions in practice are not consistently based around the HTA evidence produced by leading research institutes such as the Institute for Clinical and Economic Review.

Literature search

The bibliographic search described in Additional file 1 identified 38 papers. From this list, and papers recommended by colleagues and contacts, four seminal papers were chosen [ 5 , 7 , 17 , 18 ]. Reference lists of these 4 papers were examined and we used Google Scholar to identify the articles that cited the 4 papers. These forward and backward snowball searches identified 523 papers. Adding in the aforementioned 38 papers and eliminating duplicates provided 543 articles to be screened by title. We reviewed abstracts when titles were not enough to decide. From these, we assessed 73 full papers and decided to exclude 15. That left us with the 58 papers that we included in our review and final synthesis. These are briefly summarised in Additional file 2 . Figure 1 below shows the flow diagram.

Table  1 summarises the attributes related to innovation that were discussed in the included papers. All of the concepts of innovation discussed in the 58 papers in the literature search were covered in 5 papers: the four seminal papers [ 5 , 7 , 17 , 18 ], and one other [ 19 ]. Hence only these papers are included in Table 1 .

PRISMA flow diagram [ 58 ]

For medicines, Solà-Morales et al. (2018) [ 5 ] identified 10 dimensions of innovation in the literature which, in order of most to least widely referred to in identified papers, are: therapeutic benefit, novelty (of structure or mechanism of action), availability of existing treatment, unmet need, safety, newness, administration, clinical evidence, cost, and ‘other’.

The Advance Value Framework is a Multiple Criteria Decision Analysis (MCDA) framework for medicines proposed by Angelis & Kanavos (2017) [ 18 ]. They do not phrase a definition for innovation as such, but they do include it as one of the 5 dimensions of value that make up their framework. Their proposed notion of innovation captures the following value items: (a) medicine’s mechanism of action, (b) spill-over effects, and (c) patient usefulness (i.e. convenience).

Garrison et al. (2017) [ 17 ] include spill-over effects as one of the potential “sources of value” for health technologies. They define it as the knowledge that is produced in the process of coming up and using a particular innovative treatment that spills over to foster other innovations and benefits other patient groups. That is, the adoption of a given product with benefit for a specific group of patients produces what economists refer to as a “knowledge externality”, with spillover benefits for others. Garrison also discussed ‘real option value’. This is the value to a patient of extending their life for a limited period of time because that opens up the possibility for them to benefit from future medical advances, above and beyond the value that the immediate clinical benefit that the intervention brings to the patient.

Ciani and collaborators (2016) [ 7 ] identify three broad dimensions of innovation related to medical devices: (i) the source of innovation (demand or supply driven), (ii) the degree of discontinuity introduced (incremental or breakthrough) and (iii) the impact or consequences of innovation (measurable changes in terms of patients’ benefits, quality of the service or costs).

Ciani also discusses the ‘learning curve’ – the issue around how innovations are incorporated into routine practice, and how that can affect the measured performance of the new intervention over time. The learning curve might apply to all health technologies but it is particularly acute for non drug health technologies such as medical devices.

Mestre-Ferrandiz et al. [ 19 ] advocate for a concept of innovation that is incremental or a matter of degree, as opposed to it being a quality that is either present or not in a health technology. They characterise innovation for pharmaceuticals using 10 attributes grouped under 3 general headings: (A) Health gains, including: (1) tackling a new disease and/or indication (2); health gains measured in quality of life and/or life duration (3); faster health improvement (4); reduced side-effects and/or improved tolerability (5); reduced negative interactions with other therapies (6); treating better than current standard of care one or more different patient subpopulations; (B)(7) Patients’ / carers’ convenience; (C) Other societal gains, including cost savings: (8) releasing other healthcare resources (9); releasing other non-healthcare resources (10); productivity benefits.

How innovation is perceived, measured and rewarded in Spain, France, Italy, England and Japan

Payers and HTA bodies across the world use the `degree of innovation` as a criterion for adoption or P&R, though, in parallel with the academic literature, the meaning of this term is not precisely or consistently defined. Table  2 summarises the stated position of HTA bodies in Spain (Interministerial Medicinal Products Pricing Committee – CIPM & the Spanish Agency of Medicines and Medical Devices – AEMPS), England (National Institute for Health and Care Excellence – NICE), Italy (Agenzia Italiana del Farmaco – AIFA), France (Haute Autorité de Santé – HAS) and Japan (National Institute of Public Health – NIPH). Note that some of these institutions also hold other responsibilities than HTA, such as AIFA, which is also responsible for the regulation of medicines in Italy [ 15 ]. We classify attributes into 8 dimensions: added therapeutic value, step change, underlying health condition, safety, convenience, economic impact, evidence base, and dynamic impacts that may influence future R&D.

We used the same broad dimensions in Tables  1 and 2 , though some of the items differ. Table 1 is a summary of how the selected literature defines innovation in HTA. For instance, incremental cost-effectiveness ratio is not present in Table 1 because it was not specifically listed in the included papers. Table 2 includes all the items in Table 1 , together with the criteria used by the selected HTA bodies to capture innovation in their frameworks. Hence Table 2 shows the degree of alignment of the criteria used by HTA agencies against each other and compared with the academic literature.

In England the Kennedy report (2009) called for NICE to define innovation and for the Department of Health to regularly update their priorities for innovation in the healthcare sector [ 28 ]. This would allow stakeholders across the healthcare ecosystem to judge whether new health technologies respond to the declared needs of the system or not. NICE were encouraged to regard innovation as a social value worth pursuing independently for instance from maximizing health outcomes. As a result, NICE established 3 conditions that must be met by health technologies to be classed as innovative [ 29 ]:

The novelty condition: the technology must display “innovative characteristics” or be of an “innovative nature”.

The substantial benefits condition: the innovative nature of the technology must bring substantial health benefits to the patient, also referred to as a “‘step-change’ in the management of the condition” [ 30 ].

The demonstrable and uncounted benefits condition: the substantial benefits brought by the innovative characteristics of the health technology must not already be captured in the incremental cost-effectiveness ratio (ICER) calculation of the technology under scrutiny and they must be “demonstrable and distinctive”.

If a health technology is judged to be innovative this might justify recommending a health technology for use in the NHS with an ICER greater than £20,000/Quality Adjusted Life Year (QALY) [ 29 ].

In April 2017 AIFA implemented a new system to define and measure drug innovation [ 15 ]. The new system judges the innovativeness of a new medicine on the basis of three indicators: the level of therapeutic need that the new drug is responding to, the added therapeutic value of the new medicine compared current practice, and the quality of the clinical evidence available to support the claims of benefit of the new intervention (assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology [ 15 ]). The result can be one of three levels of innovative status: fully innovative, conditionally innovative or non-innovative. The process of reaching a conclusion about the level of innovativeness of a new drug has a deliberative component, whereby the components of the Scientific and Technical Committee (Commissione Tecnico-Scientifica, CTS) assign a level to each one of the 3 indicators of innovativeness, and then discuss the overall level of innovative status appropriate for each new drug. Depending on the level of innovativeness obtained, a new drug might benefit from access to the so-called innovative drug fund and/or immediate inclusion in regional formularies, avoiding that way any re-assessments at the regional/local level. These forms of assessment coupled with incentives for chosen technologies are meant to accelerate access to therapies deemed as innovative in the Italian healthcare system.

In France, HAS evaluates medicines and other health technologies. It considers innovation as the improvement in expected benefit (IEB) [ 31 ], taking account of the improvement in efficacy and/or safety brought by the new technology compared to others available with the same indication. Other dimensions that contribute to define innovation are taken into account in their assessment of actual clinical benefit (ACB). ACB includes the severity of the disease and the ‘public health benefit’. Public health benefit includes organizational dimensions, economic outcomes and the impact on the state of health of the population. The ACB is not comparative and it is used to determine if the new technology assessed should be reimbursed or not, while prices are negotiated on the basis of the IEB [ 24 ]. Secondary criteria for evaluating the degree of innovation include discerning between symptomatic, preventive and curative, and, for medical devices and medical equipment, HAS takes account of how disruptive the new technology is (that ‘affect existing technologies in the health field, and that may definitely replace them’) in contrast to others that might just be incrementally innovative (that only ‘show technological improvement in comparison with other devices’) [ 25 ]. However, there are no mechanisms in place specifically to reward innovations that suppose a disruptive change. There are only access-with-evidence-development schemes for devices that did not show sufficient ACB but were deemed to be of promising innovative value. Additionally, to reward innovative medicines appropriately while still collecting evidence HAS recently published their ‘Innovative medicines assessment action plan’, which expands the remit of conditional access schemes, reinforcing the use of real-world evidence to monitor medicines that have entered the market with high levels of uncertainty, fast-tracking access to promising therapies amongst other measures to better support innovation, along with other improvements in their processes [ 32 ].

In Japan, the Ministry of Health, Labour and Welfare generally reimburses all drugs and devices recommended by the Japanese regulatory agency. Pricing decisions for new health technologies are made by that same ministry but the NIPH, supported by various academic groups, coordinates the review process of the evidence submitted by manufacturers in their reimbursement applications [ 27 ]. Innovation is rewarded using a premium system, whereby new health technologies considered to be innovative are priced between 5 and 120% beyond the price of the comparator. The size of the premium is decided based on the number of the following criteria met by the new technology: (i) new mechanism of action; (ii) higher safety or efficacy; (iii) improvement of treatment for target disease, and; (iv) beneficial presentation [ 27 ].

In Spain the criteria that should be taken into account to decide whether a medicine is reimbursed by the National Healthcare System (NHS) are [ 16 ]: a) severity of the disease; b) the specific needs of certain groups of people; c) the therapeutic and social value of the medicine and incremental clinical benefit taking into account its cost-effectiveness; d) the rational use of public expenditure and the budget impact to the health service; e) the existence of therapeutic alternatives at lower price; and f) the degree of innovation of the medicine. In theory, decisions to include new medicines in the basic package covered by the National Health System, which sit with the CIPM, are made taking into account those criteria. However, the law does not define these terms or regulate how they are to be used, weighted or combined in decision-making. HTA reports also include data on safety and other factors as deemed relevant [ 22 ], but these attributes are not specifically mentioned in the P&R legislation. Despite the degree of innovation being amongst the criteria formally required for reimbursement of new medicines in Spain since 2006 [ 33 ], there is currently no definition of the concept in the public domain, nor is there a commonly accepted methodology to measure it.

For non-health technologies, the Spanish Network of Agencies for Health Technology Assessment and Services of the National Health System (RedETS) and GuíaSalud coordinate the HTA activities of the regional agencies and units in Spain and their guideline producing activities respectively, working towards the harmonization of methods applied in Spain for the assessment of health technologies and their inclusion in clinical guidelines. There are no official guidelines for how to price or reimburse non-pharmaceutical technologies. However, REdETS has published a ‘guideline for the elaboration and adaptation of rapid HTA reports’ [ 34 ], which outlines the dimensions taken into account also in full HTAs of non-drug health technologies in Spain. That is: safety, efficacy (within this efficacy dimension, there is a sub-section that captures what they refer to as patient satisfaction and acceptability), implementation considerations (economic – budget impact and efficiency of the technology –, organizational, and ethical, social and legal). This suggests that in Spain in practice, broadly speaking, similar criteria are used for medicines and non-drug health technologies, although importantly innovation is not mentioned amongst the criteria considered for non-drug health technologies.

It is worthwhile highlighting that, besides incentivizing companies to innovate by rewarding them pricing favorably and purchasing the innovations they bring to the market, states do also reward innovative companies with fiscal benefits. For instance, Spain has what they call Profarma, which is a program to stimulate the pharmaceutical sector in Spain incentivizing innovative companies with fiscal incentives. The aim is, mainly, to incentivize companies to invest in Spain, for instance setting up production and/or R&D centers there [ 35 ].

Attributes of innovation that may be used as criteria for HTA decisions

The countries analysed here can be divided into 2 groups with respect to how they define innovation. France, Japan and Italy use features such as severity, unmet need and therapeutic added value as indicators of the degree of innovation of a health technology, while England, Spain consider the degree of innovation as a separate and additional criterion from others. However, official methodological guidelines in England or Spain do not offer much guidance as to how decision makers should measure innovation, leaving such matters to the discretion of the committee members.

Hence for countries such as Spain that aim to evaluate the degree of innovation as a separate criterion, it is worthwhile to offer some clarity about which attributes of the technology are being measured. This section applies the framework of Diaby and Goeree [ 14 ] to whittle down the items identified in the literature review to a set of attributes related to innovation that could be used as criteria for HTA in the countries of interest. Spain is taken as a “case study”, though the general approach is meant to be generalisable to other jurisdictions.

A comprehensive’ set of decision-making criteria would encompass all the dimensions listed in Table 2 . The legislation in Spain does not mention step-change’, convenience’, strength of evidence base’ or impact on future R&D’ as criteria. This does not mean these items are ignored in HTA in Spain, only that they are not explicitly listed, and so we take these dimensions forward as candidates for inclusion in the category of ‘innovation’ for Spain. A comprehensive set of criteria would also be applicable to both medicines and other technologies. In some cases this can be achieved by tweaking the definition. For example, novelty refers to new drug structures or mechanisms of action, but it could very well refer to innovative mechanical architectures in the case of a device.

‘Value relevance’ refers in this context to whether a particular candidate item reflects the preferences of decision-makers with regard to the level of innovation in a product. Decisions makers in each jurisdiction would have to judge whether a given item is relevant and important to the decision problem at hand.

‘Non-redundancy’ refers to whether criteria are all necessary and do not repeat, double-count or overlap. NICE recognise this by requiring that benefits brought by the innovative characteristics of the health technology must not already be captured in other dimensions. For example, if the novel mode of administration leads to better adherence and hence greater effectiveness, this benefit should not be double-counted both in `added therapeutic value’ and in ‘patient convenience’. ‘Independence’ requires that the items are mutually exclusive, such that the level of performance in one item does not influence assessments about others.

Decision-makers must have a common understanding of what the criteria aim to measure to achieve precision and legitimacy. The items in Table 2 seem mostly self-explanatory, possibly with the exception of spill-over effects and real-option value. These items are rather abstract and might require explanation for decision-makers.

‘Measurement’ of each item does not have to be necessarily quantitative, but must be sufficiently rigourous and reproducible to avoid bias and achieve a reasonable degree of precision. Decision makers in HTA already have tools for measuring some of the items that might constitute a criterion of innovation. Where products promise a ‘step-change’, regulators (e.g. the Food & Drug Administration (FDA) in the United States, or European Medicines Agency (EMA) in Europe) may enable priority designation policies and accelerated access pathways, for devices [ 36 ], therapies generally [ 37 , 38 ] and for specific cases such as gene therapies [ 39 ]. The strength of the evidence base is commonly assessed by applying a hierarchy of evidence [ 40 ] and where relevant might also capture uncertainties related to the learning curve [ 41 ]. There are a variety of instruments and outcome measures for patient convenience, though these are not comprehensive or easily transferable between patient groups or technology types. There is some theoretical work on how real option value might be measured, though it has yet to be validated in practice [ 42 ]. Spill-over would be challenging to measure as a HTA criteria.

Towards a concept of innovation in Spain

The concept of innovation in healthcare has been widely described and discussed in the literature. However, rarely has it been done thinking about how different countries could go about defining a concept that fits with their HTA systems, to then be able to measure it and incorporate it in their methods guides and their assessments of different types of health technologies. It has been argued in the past that, although there might be distinct features of innovation worth rewarding distinctively, it would only be advisable to do so if innovation can be defined clearly and distinctively enough from other value dimensions already accounted for in the system, and if sustainable ways of rewarding innovativeness can be devised [ 43 ]. In this paper, the use of a case study allows us to point to how this concept might be tailored to a particular HTA system.

Our findings suggest that the following dimensions might be candidates for a criterion of innovation, at least in the context of HTA in Spain: ‘step-change’, ‘convenience’, ‘strength of evidence base’ and ‘impact on future R&D′. Of these, the concepts of step-change and strength of evidence base appear to be most straightforward to measure using existing instruments and procedures. However, in the context of innovative technologies, they are in some instances not entirely mutually exclusive. For ‘step-change’, regulators have designations such as ‘breakthrough’, and ‘fast-track’ that indicate serious conditions with a potential for significant improvement or unmet need, but at the same time high uncertainty. The evidence base may be undeveloped or weak, leading regulators to require further evidence collection as a condition of approval. HTA decision makers may also wish to stipulate further evidentiary or conditional reimbursement conditions for adoption into national health systems. The relevance of items such as ‘convenience’ or ‘novel mode of administration’ depends on context, though it is important to avoid double counting benefits and to apply such criteria consistently across different indications and interventions. Undoubtedly the most abstract and difficult to measure are items related to the interaction between current adoption decisions and the direction of future R&D. Novelty per-se might be seen as a necessary but not sufficient condition for recognising a technology as innovative, apart from specific circumstances such as an option for patients who are contra-indicated for existing interventions. Real –option value also would only be applicable in very specific circumstances, where patients need to buy time until they can take advantage of another new therapy just on the horizon. Scientific spillover effects are quite abstract and diffuse. R&D investment is a global enterprise influenced by a multitude of factors, and HTA decision-making procedures in individual countries and individual indications may have only a marginal impact, if any. However, there may be specific contexts where scientific advance is propelled forward by synergistic achievements in related areas, such as gene or cell therapies, and this might be usefully recognised at national level.

A change in HTA criteria requires transparency, robustness and an integrative process that gives the opportunity to different stakeholders to present their perspectives [ 44 ]. MCDA could and has been used to measure the degree of innovation [ 18 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ], to weight the different items to produce an overall innovation score and/or weight the importance of innovation relative to other value dimensions. However, it can be a complex method, data hungry and challenging to use routinely adhering to good practice guidelines [ 53 ], particularly by smaller HTA bodies, though the challenges are not insurmountable. A more pragmatic approach could be the use of a checklist, which is something that has already been done for other purposes in HTA [ 54 ].

Research into the extent to which innovation is actually captured and used in practice in decision making in HTA suggests that it is indeed taken into account in decision making, and in fact it is referred to by NICE with a high frequency relative to other criteria in their appraisal documents [ 55 ]. However, it does not rank between the most relevant criteria for most decision makers from across the world [ 56 ]. An interesting step further would be to explore the societal (i.e. public’s) preferences for innovation [ 57 ] in any country considering its inclusion in their HTA systems.

If innovation is to be used as operational criteria for adoption and P&R of health technologies, the concept must be clearly defined, and it ought to be independent from other value dimensions already captured in HTA systems. We acknowledge that, in the present paper, we have only superficially touched upon these ways of enabling innovation in health technology assessment, and further research would be to work with decision makers to produce a practical framework.

Availability of data and materials

All data generated or analysed during this study are included in this published article [and its supplementary information files].

Abbreviations

Actual Clinical Benefit

Agenzia Italiana del Farmaco

Commissione Tecnico-Scientifica

European Medicines Agency

Food & Drug Administration

Grading of Recommendations Assessment, Development and Evaluation

Haute Autorité de Santé

Health Technology Assessment

Improvement in Expected Benefit

Incremental Cost-Effectiveness Ratio

Interministerial Medicinal Products Pricing Committee

Multiple Criteria Decision Analysis

National Healthcare System

National Institute for Health and Care Excellence

National Institute of Public Health

Pricing & Reimbursement

Quality Adjusted Life Year

Research and Development

Spanish Agency of Medicines and Medical Devices

Spanish Network of Agencies for Health Technology Assessment and Services of the National Health System

World Health Organization

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Acknowledgements

We thank Maria Piedad Rosario Lozano for her assistance and suggestions for the search strategy we used for this paper, which greatly improved the manuscript.

This study has received support from researchproject PID2019.105597RA.I00 financed by the Spanish Ministry of Science and Innovation/ National Research Agency MCIN/ AEI/10.13039/501100011033. The funders had no role in the study.

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Rejon-Parrilla, J.C., Espin, J. & Epstein, D. How innovation can be defined, evaluated and rewarded in health technology assessment. Health Econ Rev 12 , 1 (2022). https://doi.org/10.1186/s13561-021-00342-y

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The potential for artificial intelligence to transform healthcare: perspectives from international health leaders

• Christina Silcox 1 ,
• Eyal Zimlichmann 2 , 3 ,
• Katie Huber   ORCID: orcid.org/0000-0003-2519-8714 1 ,
• Neil Rowen 1 ,
• Robert Saunders 1 ,
• Mark McClellan 1 ,
• Charles N. Kahn III 3 , 4 ,
• Claudia A. Salzberg 3 &
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Artificial intelligence (AI) has the potential to transform care delivery by improving health outcomes, patient safety, and the affordability and accessibility of high-quality care. AI will be critical to building an infrastructure capable of caring for an increasingly aging population, utilizing an ever-increasing knowledge of disease and options for precision treatments, and combatting workforce shortages and burnout of medical professionals. However, we are not currently on track to create this future. This is in part because the health data needed to train, test, use, and surveil these tools are generally neither standardized nor accessible. There is also universal concern about the ability to monitor health AI tools for changes in performance as they are implemented in new places, used with diverse populations, and over time as health data may change. The Future of Health (FOH), an international community of senior health care leaders, collaborated with the Duke-Margolis Institute for Health Policy to conduct a literature review, expert convening, and consensus-building exercise around this topic. This commentary summarizes the four priority action areas and recommendations for health care organizations and policymakers across the globe that FOH members identified as important for fully realizing AI’s potential in health care: improving data quality to power AI, building infrastructure to encourage efficient and trustworthy development and evaluations, sharing data for better AI, and providing incentives to accelerate the progress and impact of AI.

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Introduction.

Artificial intelligence (AI), supported by timely and accurate data and evidence, has the potential to transform health care delivery by improving health outcomes, patient safety, and the affordability and accessibility of high-quality care 1 , 2 . AI integration is critical to building an infrastructure capable of caring for an increasingly aging population, utilizing an ever-increasing knowledge of disease and options for precision treatments, and combatting workforce shortages and burnout of medical professionals. However, we are not currently on track to create this future. This is in part because the health data needed to train, test, use, and surveil these tools are generally neither standardized nor accessible. This is true across the international community, although there is variable progress within individual countries. There is also universal concern about monitoring health AI tools for changes in performance as they are implemented in new places, used with diverse populations, and over time as health data may change.

The Future of Health (FOH) is an international community of senior health care leaders representing health systems, health policy, health care technology, venture funding, insurance, and risk management. FOH collaborated with the Duke-Margolis Institute for Health Policy to conduct a literature review, expert convening, and consensus-building exercise. In total, 46 senior health care leaders were engaged in this work, from eleven countries in Europe, North America, Africa, Asia, and Australia. This commentary summarizes the four priority action areas and recommendations for health care organizations and policymakers that FOH members identified as important for fully realizing AI’s potential in health care: improving data quality to power AI, building infrastructure to encourage efficient and trustworthy development and evaluations, sharing data for better AI, and providing incentives to accelerate the progress and impact of AI.

Powering AI through high-quality data

“Going forward, data are going to be the most valuable commodity in health care. Organizations need robust plans about how to mobilize and use their data.”

AI algorithms will only perform as well as the accuracy and completeness of key underlying data, and data quality is dependent on actions and workflows that encourage trust.

To begin to improve data quality, FOH members agreed that an initial priority is identifying and assuring reliable availability of high-priority data elements for promising AI applications: those with the most predictive value, those of the highest value to patients, and those most important for analyses of performance, including subgroup analyses to detect bias.

Leaders should also advocate for aligned policy incentives to improve the availability and reliability of these priority data elements. There are several examples of efforts across the world to identify and standardize high-priority data elements for AI applications and beyond, such as the multinational project STANDING Together, which is developing standards to improve the quality and representativeness of data used to build and test AI tools 3 .

Policy incentives that would further encourage high-quality data collection include (1) aligned payment incentives for measures of health care quality and safety, and ensuring the reliability of the underlying data, and (2) quality measures and performance standards focused on the reliability, completeness, and timeliness of collection and sharing of high-priority data itself.

Trust and verify

“Your AI algorithms are only going to be as good as the data and the real-world evidence used to validate them, and the data are only going to be as good as the trust and privacy and supporting policies.”

FOH members stressed the importance of showing that AI tools are both effective and safe within their specific patient populations.

This is a particular challenge with AI tools, whose performance can differ dramatically across sites and over time, as health data patterns and population characteristics vary. For example, several studies of the Epic Sepsis Model found both location-based differences in performance and degradation in performance over time due to data drift 4 , 5 . However, real-world evaluations are often much more difficult for algorithms that are used for longer-term predictions, or to avert long-term complications from occurring, particularly in the absence of connected, longitudinal data infrastructure. As such, health systems must prioritize implementing data standards and data infrastructure that can facilitate the retraining or tuning of algorithms, test for local performance and bias, and ensure scalability across the organization and longer-term applications 6 .

There are efforts to help leaders and health systems develop consensus-based evaluation techniques and infrastructure for AI tools, including HealthAI: The Global Agency for Responsible AI in Health, which aims to build and certify validation mechanisms for nations and regions to adopt; and the Coalition for Health AI (CHAI), which recently announced plans to build a US-wide health AI assurance labs network 7 , 8 . These efforts, if successful, will assist manufacturers and health systems in complying with new laws, rules, and regulations being proposed and released that seek to ensure AI tools are trustworthy, such as the EU AI Act and the 2023 US Executive Order on AI.

Sharing data for better AI

“Underlying these challenges is the investment required to standardize business processes so that you actually get data that’s usable between institutions and even within an institution.”

While high-quality internal data may enable some types of AI-tool development and testing, this is insufficient to power and evaluate all AI applications. To build truly effective AI-enabled predictive software for clinical care and predictive supports, data often need to be interoperable across health systems to build a diverse picture of patients’ health across geographies, and reliably shared.

FOH members recommended that health care leaders work with researchers and policymakers to connect detailed encounter data with longitudinal outcomes, and pilot opportunities across diverse populations and systems to help assure valid outcome evaluations as well as address potential confounding and population subgroup differences—the ability to aggregate data is a clear rate-limiting step. The South African National Digital Health Strategy outlined interventions to improve the adoption of digital technologies while complying with the 2013 Protection of Personal Information Act 9 . Although challenges remain, the country has made progress on multiple fronts, including building out a Health Patient Registration System as a first step towards a portable, longitudinal patient record system and releasing a Health Normative Standards Framework to improve data flow across institutional and geographic boundaries 10 .

Leaders should adopt policies in their organizations, and encourage adoption in their province and country, that simplify data governance and sharing while providing appropriate privacy protections – including building foundations of trust with patients and the public as previously discussed. Privacy-preserving innovations include ways to “share” data without movement from protected systems using approaches like federated analyses, data sandboxes, or synthetic data. In addition to exploring privacy-preserving approaches to data sharing, countries and health systems may need to consider broad and dynamic approaches to consent 11 , 12 . As we look to a future where a patient may have thousands of algorithms churning away at their data, efforts to improve data quality and sharing should include enabling patients’ access to and engagement with their own data to encourage them to actively partner in their health and provide transparency on how their data are being used to improve health care. For example, the Understanding Patient Data program in the United Kingdom produces research and resources to explain how the National Health Service uses patients’ data 13 . Community engagement efforts can further assist with these efforts by building trust and expanding understanding.

FOH members also stressed the importance of timely data access. Health systems should work together to establish re-usable governance and privacy frameworks that allow stakeholders to clearly understand what data will be shared and how it will be protected to reduce the time needed for data use agreements. Trusted third-party data coordinating centers could also be used to set up “precertification” systems around data quality, testing, and cybersecurity to support health organizations with appropriate data stewardship to form partnerships and access data rapidly.

Incentivizing progress for AI impact

“Unless it’s tied to some kind of compensation to the organization, the drive to help implement those tools and overcome that risk aversion is going to be very high… I do think that business driver needs to be there.”

AI tools and data quality initiatives have not moved as quickly in health care due to the lack of direct payment, and often, misalignment of financial incentives and supports for high-quality data collection and predictive analytics. This affects both the ability to purchase and safely implement commercial AI products as well as the development of “homegrown” AI tools.

FOH members recommended that leaders should advocate for paying for value in health – quality, safety, better health, and lower costs for patients. This better aligns the financial incentives for accelerating the development, evaluation, and adoption of AI as well as other tools designed to either keep patients healthy or quickly diagnose and treat them with the most effective therapies when they do become ill. Effective personalized health care requires high-quality, standardized, interoperable datasets from diverse sources 14 . Within value-based payments themselves, data are critical to measuring quality of care and patient outcomes, adjusted or contextualized for factors outside of clinical control. Value-based payments therefore align incentives for (1) high-quality data collection and trusted use, (2) building effective AI tools, and (3) ensuring that those tools are improving patient outcomes and/or health system operations.

Data have become the most valuable commodity in health care, but questions remain about whether there will be an AI “revolution” or “evolution” in health care delivery. Early AI applications in certain clinical areas have been promising, but more advanced AI tools will require higher quality, real-world data that is interoperable and secure. The steps health care organization leaders and policymakers take in the coming years, starting with short-term opportunities to develop meaningful AI applications that achieve measurable improvements in outcomes and costs, will be critical in enabling this future that can improve health outcomes, safety, affordability, and equity.

Data availability

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

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Acknowledgements

The authors acknowledge Oranit Ido and Jonathan Gonzalez-Smith for their contributions to this work. This study was funded by The Future of Health, LLC. The Future of Health, LLC, was involved in all stages of this research, including study design, data collection, analysis and interpretation of data, and the preparation of this manuscript.

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C.S., K.H., N.R., and R.S. conducted initial background research and analyzed qualitative data from stakeholders. All authors (C.S., E.Z., K.H., N.R., R.S., M.M., C.K., C.A.S., and D.B.) assisted with conceptualization of the project and strategic guidance. C.S., K.H., and N.R. wrote initial drafts of the manuscript. All authors contributed to critical revisions of the manuscript and read and approved the final manuscript.

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C.S., K.H., N.R., and C.A.S. declare no competing interests. E.Z. reports personal fees from Arkin Holdings, personal fees from Statista and equity from Valera Health, Profility and Hello Heart. R.S. has been an external reviewer for The John A. Hartford Foundation, and is a co-chair for the Health Evolution Summit Roundtable on Value-Based Care for Specialized Populations. M.M. is an independent director on the boards of Johnson & Johnson, Cigna, Alignment Healthcare, and PrognomIQ; co-chairs the Guiding Committee for the Health Care Payment Learning and Action Network; and reports fees for serving as an adviser for Arsenal Capital Partners, Blackstone Life Sciences, and MITRE. C.K. is a Profility Board member and additionally reports equity from Valera Health and MDClone. D.W.B. reports grants and personal fees from EarlySense, personal fees from CDI Negev, equity from Valera Health, equity from Clew, equity from MDClone, personal fees and equity from AESOP, personal fees and equity from Feelbetter, equity from Guided Clinical Solutions, and grants from IBM Watson Health, outside the submitted work. D.W.B. has a patent pending (PHC-028564 US PCT), on intraoperative clinical decision support.

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Silcox, C., Zimlichmann, E., Huber, K. et al. The potential for artificial intelligence to transform healthcare: perspectives from international health leaders. npj Digit. Med. 7 , 88 (2024). https://doi.org/10.1038/s41746-024-01097-6

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5 innovations that are revolutionizing global healthcare

Technological advances are starting to revolutionize the healthcare sector. Image:  Pexels/Chokniti Khongchum

.chakra .wef-1c7l3mo{-webkit-transition:all 0.15s ease-out;transition:all 0.15s ease-out;cursor:pointer;-webkit-text-decoration:none;text-decoration:none;outline:none;color:inherit;}.chakra .wef-1c7l3mo:hover,.chakra .wef-1c7l3mo[data-hover]{-webkit-text-decoration:underline;text-decoration:underline;}.chakra .wef-1c7l3mo:focus,.chakra .wef-1c7l3mo[data-focus]{box-shadow:0 0 0 3px rgba(168,203,251,0.5);} Stefan Ellerbeck

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• Healthcare innovation is accelerating at an unprecedented scale, particularly in the digital sphere, the World Health Organization says.
• Advances such as artificial intelligence and gene editing are transforming the way diseases are detected and treated.
• Here are 5 innovations that are pushing boundaries in healthcare.

Suppose you or someone you know needs surgery or treatment for an illness or disease. In that case, it’s increasingly likely that advances in medical technology will improve the chances of a successful outcome.

Medical innovations have occurred throughout history, continually advancing our ability to treat complex diseases. These include the first vaccine for smallpox in the 18th century, the development of antibiotics in the 1920s and the world’s first organ transplant three decades later.

However, the 21st century is bringing even more progress, with technological advances revolutionising the healthcare sector. The World Health Organization says innovation, particularly in the digital sphere, is taking place at an unprecedented scale.

Innovations that are transforming the global healthcare industry

Here are five innovations that are pushing even more boundaries in healthcare.

Artificial intelligence (AI)

The use of algorithms and machine learning in detecting, diagnosing and treating disease has become a significant area of life sciences. Some believe it is the biggest healthcare revolution of the 21st century.

AI can detect diseases early and make more accurate diagnoses more quickly than conventional means. In breast cancer, AI is enabling mammograms to be reviewed 30 times faster with almost 100% accuracy , reducing the need for biopsies.

Meanwhile, a deep-learning algorithm developed by health-tech company Qure.ai is enabling the early detection of lung cancer . The firm says a study demonstrated a 17% improvement when using AI to interpret chest x-rays compared to conventional radiology readings. It has formed a partnership with drug giant AstraZeneca that aims to scale up the technology to reduce lung cancer mortality rates around the world.

3D printing

The use of 3D printing techniques in healthcare is growing rapidly. More than 110 hospitals in the US had facilities for point-of-care 3D manufacturing in 2019, compared with just 3 in 2010, according to data provided by Statista.

Number of US hospitals with a centralized 3D printing facility

The technology is being used for creating dental implants, replacement joints, as well as for made-to-measure prosthetics. Research into using 3D printers for manufacturing skin tissue, organs and even medication is also underway.

One of the main benefits of 3D printing is that it greatly accelerates production processes and, therefore, also reduces the cost of traditionally manufactured products. The technology has reduced the time it takes to produce hearing aids from more than one week to just one day, according to the American Hospital Association.

CRISPR gene editing

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) gene-editing technology can potentially transform how diseases are treated. It could help make significant advances against killer diseases like cancer and HIV in a matter of years.

The technology works by “harnessing the natural mechanisms” of invading viruses and then “cutting out” infected DNA strands . By altering cell mutations, CRISPR also has the potential to transform the way rare conditions like cystic fibrosis and sickle cell disease are treated.

However, ethical concerns around its use need to be addressed, as its potential ability to change genomes in children has been raised. A team of scientists was prosecuted in China in 2020 after they claimed to have created the world’s first “designer babies” using CRISPR.

The Global Health and Strategic Outlook 2023 highlighted that there will be an estimated shortage of 10 million healthcare workers worldwide by 2030.

The World Economic Forum’s Centre for Health and Healthcare works with governments and businesses to build more resilient, efficient and equitable healthcare systems that embrace new technologies.

Learn more about our impact:

• Global vaccine delivery: Our contribution to COVAX resulted in the delivery of over 1 billion COVID-19 vaccines and our efforts in launching Gavi, the Vaccine Alliance, has helped save more than 13 million lives over the past 20 years .
• Davos Alzheimer's Collaborative: Through this collaborative initiative, we are working to accelerate progress in the discovery, testing and delivery of interventions for Alzheimer's – building a cohort of 1 million people living with the disease who provide real-world data to researchers worldwide.
• Mental health policy: In partnership with Deloitte, we developed a comprehensive toolkit to assist lawmakers in crafting effective policies related to technology for mental health .
• Global Coalition for Value in Healthcare: We are fostering a sustainable and equitable healthcare industry by launching innovative healthcare hubs to address ineffective spending on global health . In the Netherlands, for example, it has provided care for more than 3,000 patients with type 1 diabetes and enrolled 69 healthcare providers who supported 50,000 mothers in Sub-Saharan Africa.
• UHC2030 Private Sector Constituency : This collaboration with 30 diverse stakeholders plays a crucial role in advocating for universal health coverage and emphasizing the private sector's potential to contribute to achieving this ambitious goal.

Want to know more about our centre’s impact or get involved? Contact us .

Virtual reality (VR)

The VR and AR (augmented reality) market is booming worldwide , and both technologies are being used increasingly in healthcare applications. The technology can be deployed in various ways , such as performing more advanced surgery, helping with pain relief, and treating mental health conditions.

Surgeons can also use a VR helmet to rehearse procedures, as well as to have full sight of the inside of a patient's body. And the technology can help people to "unlearn" chronic pain by retraining the brain, Forbes says.

VR can also help people with mental disorders overcome their fears by providing them a controlled environment for social interactions. Two hours of exposure to treatment for fear of heights cut patient anxiety by an average of 68%, according to Forbes.

Smart bandages

A bandage that uses sensors to monitor wound healing has been developed by researchers in the US. It “promotes faster closure of wounds, increases new blood flow to injured tissue, and enhances skin recovery by significantly reducing scar formation”, according to the Stanford University team behind it.

A thin electronic layer on the bandage has temperature sensors that monitor a wound. If necessary, they can trigger more electrical stimulation to accelerate tissue closure.

“With stimulation and sensing in one device, the smart bandage speeds healing, but it also keeps track as the wound is improving,” said Artem Trotsyuk, co-author of a study of the bandage.

The device needs to overcome cost and data storage issues before going into mass production. However, it could eventually offer significant help to people with suppressed immune systems and diseases like diabetes, who often suffer from slow-healing wounds.

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Innovation in Medicine and Healthcare

Proceedings of 11th KES-InMed 2023

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Table of contents (35 papers)

Front matter, innovations in assessment, education, and interventions for children with developmental disorders, online platforms and applications for the development and treatment of reading skills in children - a comparison between three countries.

• Polina Mihova, Maria Mavrothanasi, Haneen Alshawesh, Tsveta Stoyanova

Development of Three Language Digital Platform for Early Childhood Development Screening PTES – Preliminary Parents Self-check Results

• Nina Iordanova, Kornilia Tsoukka, Maria Mavrothanasi

Online Parent Counselling in Speech and Language Therapy - The View of the Professionals

• Margarita Stankova, Tsveta Kamenski, Kornilia Tsoukka, Haneen Alshawesh, Alexandros Proedrou

Data Driven Models for Health Care Systems

Modelling competing risk for stroke survival data.

• Virgilijus Sakalauskas, Dalia Kriksciuniene

Artificial Intelligence Chatbots and Conversational Agents – An Overview of Clinical Studies in Health Care

• I. Stević, D. Vukmirović, V. Vujović, V. Marinković

Challenges and Solutions for Artificial Intelligence Adoption in Healthcare – A Literature Review

• Uldis Donins, Daiga Behmane

An Explainable Deep Network Framework with Case-Based Reasoning Strategies for Survival Analysis in Oncology

• Isabelle Bichindaritz, Guanghui Liu

Projects, Systems, and Applications of Smart Medicine/Healthcare

Healthcare under society 5.0: a systematic literature review of applications and case studies.

• Jean Paul Sebastian Piest, Yoshimasa Masuda, Osamu Nakamura

Strategic Risk Management for Low-Code Development Platforms with Enterprise Architecture Approach: Case of Global Pharmaceutical Enterprise

• Tetsuro Miyake, Yoshimasa Masuda, Akiko Oguchi, Atsushi Ishida

Optimized Deployment Planning for Elderly Welfare Facilities Based on Network Theory – A Case Study of Ibaraki Prefecture’s Day Care Welfare Facilities for the Elderly

• Xiangchu Chen, Sumika Arima

Characterizing Groups of Patients with Depression using Clustering Techniques

• Ma. Sheila A. Magboo, Vincent Peter C. Magboo

Automatic Recognition of Check-Points for NCPR Video Review

• Noboru Nihshimoto, Mitsuhito Ando, Haruo Noma, Kogoro Iwanaga

Speech Recognition in Healthcare: A Comparison of Different Speech Recognition Input Interactions

• Eugenio Corbisiero, Gennaro Costagliola, Mattia De Rosa, Vittorio Fuccella, Alfonso Piscitelli, Parinaz Tabari

Medical Image Processing

Improved diagnostic accuracy of rheumatoid arthritic by image generation using stylegan2.

• Tomio Goto, Reo Kojima, Koji Funahashi

BrainFuse: Self-Supervised Data Fusion Augmentation for Brain MRI’s via Frame Interpolation

• Robert Herscovici, Cristian Simionescu

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Book Title : Innovation in Medicine and Healthcare

Book Subtitle : Proceedings of 11th KES-InMed 2023

Editors : Yen-Wei Chen, Satoshi Tanaka, R. J. Howlett, Lakhmi C. Jain

Series Title : Smart Innovation, Systems and Technologies

DOI : https://doi.org/10.1007/978-981-99-3311-2

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Number of Pages : XVI, 396

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Topics : Computational Intelligence , Health Informatics , Artificial Intelligence

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Research ethics and artificial intelligence for global health: perspectives from the global forum on bioethics in research

• James Shaw 1 , 13 ,
• Joseph Ali 2 , 3 ,
• Caesar A. Atuire 4 , 5 ,
• Phaik Yeong Cheah 6 ,
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• Judy Wawira Gichoya 8 ,
• Adrienne Hunt 9 ,
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The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice. In this paper we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022.

The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, research ethics committee members and other actors to engage with challenges and opportunities specifically related to research ethics. In 2022 the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations, 16 governance presentations, and a series of small group and large group discussions. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. In this paper, we highlight central insights arising from GFBR 2022.

We describe the significance of four thematic insights arising from the forum: (1) Appropriateness of building AI, (2) Transferability of AI systems, (3) Accountability for AI decision-making and outcomes, and (4) Individual consent. We then describe eight recommendations for governance leaders to enhance the ethical governance of AI in global health research, addressing issues such as AI impact assessments, environmental values, and fair partnerships.

Conclusions

The 2022 Global Forum on Bioethics in Research illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research.

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Introduction

The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice [ 1 , 2 , 3 ]. Beyond the growing number of AI applications being implemented in health care, capabilities of AI models such as Large Language Models (LLMs) expand the potential reach and significance of AI technologies across health-related fields [ 4 , 5 ]. Discussion about effective, ethical governance of AI technologies has spanned a range of governance approaches, including government regulation, organizational decision-making, professional self-regulation, and research ethics review [ 6 , 7 , 8 ]. In this paper, we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health research, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022. Although applications of AI for research, health care, and public health are diverse and advancing rapidly, the insights generated at the forum remain highly relevant from a global health perspective. After summarizing important context for work in this domain, we highlight categories of ethical issues emphasized at the forum for attention from a research ethics perspective internationally. We then outline strategies proposed for research, innovation, and governance to support more ethical AI for global health.

In this paper, we adopt the definition of AI systems provided by the Organization for Economic Cooperation and Development (OECD) as our starting point. Their definition states that an AI system is “a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. AI systems are designed to operate with varying levels of autonomy” [ 9 ]. The conceptualization of an algorithm as helping to constitute an AI system, along with hardware, other elements of software, and a particular context of use, illustrates the wide variety of ways in which AI can be applied. We have found it useful to differentiate applications of AI in research as those classified as “AI systems for discovery” and “AI systems for intervention”. An AI system for discovery is one that is intended to generate new knowledge, for example in drug discovery or public health research in which researchers are seeking potential targets for intervention, innovation, or further research. An AI system for intervention is one that directly contributes to enacting an intervention in a particular context, for example informing decision-making at the point of care or assisting with accuracy in a surgical procedure.

The mandate of the GFBR is to take a broad view of what constitutes research and its regulation in global health, with special attention to bioethics in Low- and Middle- Income Countries. AI as a group of technologies demands such a broad view. AI development for health occurs in a variety of environments, including universities and academic health sciences centers where research ethics review remains an important element of the governance of science and innovation internationally [ 10 , 11 ]. In these settings, research ethics committees (RECs; also known by different names such as Institutional Review Boards or IRBs) make decisions about the ethical appropriateness of projects proposed by researchers and other institutional members, ultimately determining whether a given project is allowed to proceed on ethical grounds [ 12 ].

However, research involving AI for health also takes place in large corporations and smaller scale start-ups, which in some jurisdictions fall outside the scope of research ethics regulation. In the domain of AI, the question of what constitutes research also becomes blurred. For example, is the development of an algorithm itself considered a part of the research process? Or only when that algorithm is tested under the formal constraints of a systematic research methodology? In this paper we take an inclusive view, in which AI development is included in the definition of research activity and within scope for our inquiry, regardless of the setting in which it takes place. This broad perspective characterizes the approach to “research ethics” we take in this paper, extending beyond the work of RECs to include the ethical analysis of the wide range of activities that constitute research as the generation of new knowledge and intervention in the world.

Ethical governance of AI in global health

The ethical governance of AI for global health has been widely discussed in recent years. The World Health Organization (WHO) released its guidelines on ethics and governance of AI for health in 2021, endorsing a set of six ethical principles and exploring the relevance of those principles through a variety of use cases. The WHO guidelines also provided an overview of AI governance, defining governance as covering “a range of steering and rule-making functions of governments and other decision-makers, including international health agencies, for the achievement of national health policy objectives conducive to universal health coverage.” (p. 81) The report usefully provided a series of recommendations related to governance of seven domains pertaining to AI for health: data, benefit sharing, the private sector, the public sector, regulation, policy observatories/model legislation, and global governance. The report acknowledges that much work is yet to be done to advance international cooperation on AI governance, especially related to prioritizing voices from Low- and Middle-Income Countries (LMICs) in global dialogue.

One important point emphasized in the WHO report that reinforces the broader literature on global governance of AI is the distribution of responsibility across a wide range of actors in the AI ecosystem. This is especially important to highlight when focused on research for global health, which is specifically about work that transcends national borders. Alami et al. (2020) discussed the unique risks raised by AI research in global health, ranging from the unavailability of data in many LMICs required to train locally relevant AI models to the capacity of health systems to absorb new AI technologies that demand the use of resources from elsewhere in the system. These observations illustrate the need to identify the unique issues posed by AI research for global health specifically, and the strategies that can be employed by all those implicated in AI governance to promote ethically responsible use of AI in global health research.

RECs and the regulation of research involving AI

RECs represent an important element of the governance of AI for global health research, and thus warrant further commentary as background to our paper. Despite the importance of RECs, foundational questions have been raised about their capabilities to accurately understand and address ethical issues raised by studies involving AI. Rahimzadeh et al. (2023) outlined how RECs in the United States are under-prepared to align with recent federal policy requiring that RECs review data sharing and management plans with attention to the unique ethical issues raised in AI research for health [ 13 ]. Similar research in South Africa identified variability in understanding of existing regulations and ethical issues associated with health-related big data sharing and management among research ethics committee members [ 14 , 15 ]. The effort to address harms accruing to groups or communities as opposed to individuals whose data are included in AI research has also been identified as a unique challenge for RECs [ 16 , 17 ]. Doerr and Meeder (2022) suggested that current regulatory frameworks for research ethics might actually prevent RECs from adequately addressing such issues, as they are deemed out of scope of REC review [ 16 ]. Furthermore, research in the United Kingdom and Canada has suggested that researchers using AI methods for health tend to distinguish between ethical issues and social impact of their research, adopting an overly narrow view of what constitutes ethical issues in their work [ 18 ].

The challenges for RECs in adequately addressing ethical issues in AI research for health care and public health exceed a straightforward survey of ethical considerations. As Ferretti et al. (2021) contend, some capabilities of RECs adequately cover certain issues in AI-based health research, such as the common occurrence of conflicts of interest where researchers who accept funds from commercial technology providers are implicitly incentivized to produce results that align with commercial interests [ 12 ]. However, some features of REC review require reform to adequately meet ethical needs. Ferretti et al. outlined weaknesses of RECs that are longstanding and those that are novel to AI-related projects, proposing a series of directions for development that are regulatory, procedural, and complementary to REC functionality. The work required on a global scale to update the REC function in response to the demands of research involving AI is substantial.

These issues take greater urgency in the context of global health [ 19 ]. Teixeira da Silva (2022) described the global practice of “ethics dumping”, where researchers from high income countries bring ethically contentious practices to RECs in low-income countries as a strategy to gain approval and move projects forward [ 20 ]. Although not yet systematically documented in AI research for health, risk of ethics dumping in AI research is high. Evidence is already emerging of practices of “health data colonialism”, in which AI researchers and developers from large organizations in high-income countries acquire data to build algorithms in LMICs to avoid stricter regulations [ 21 ]. This specific practice is part of a larger collection of practices that characterize health data colonialism, involving the broader exploitation of data and the populations they represent primarily for commercial gain [ 21 , 22 ]. As an additional complication, AI algorithms trained on data from high-income contexts are unlikely to apply in straightforward ways to LMIC settings [ 21 , 23 ]. In the context of global health, there is widespread acknowledgement about the need to not only enhance the knowledge base of REC members about AI-based methods internationally, but to acknowledge the broader shifts required to encourage their capabilities to more fully address these and other ethical issues associated with AI research for health [ 8 ].

Although RECs are an important part of the story of the ethical governance of AI for global health research, they are not the only part. The responsibilities of supra-national entities such as the World Health Organization, national governments, organizational leaders, commercial AI technology providers, health care professionals, and other groups continue to be worked out internationally. In this context of ongoing work, examining issues that demand attention and strategies to address them remains an urgent and valuable task.

The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, REC members and other actors to engage with challenges and opportunities specifically related to research ethics. Each year the GFBR meeting includes a series of case studies and keynotes presented in plenary format to an audience of approximately 100 people who have applied and been competitively selected to attend, along with small-group breakout discussions to advance thinking on related issues. The specific topic of the forum changes each year, with past topics including ethical issues in research with people living with mental health conditions (2021), genome editing (2019), and biobanking/data sharing (2018). The forum is intended to remain grounded in the practical challenges of engaging in research ethics, with special interest in low resource settings from a global health perspective. A post-meeting fellowship scheme is open to all LMIC participants, providing a unique opportunity to apply for funding to further explore and address the ethical challenges that are identified during the meeting.

In 2022, the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations (both short and long form) reporting on specific initiatives related to research ethics and AI for health, and 16 governance presentations (both short and long form) reporting on actual approaches to governing AI in different country settings. A keynote presentation from Professor Effy Vayena addressed the topic of the broader context for AI ethics in a rapidly evolving field. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. The 2-day forum addressed a wide range of themes. The conference report provides a detailed overview of each of the specific topics addressed while a policy paper outlines the cross-cutting themes (both documents are available at the GFBR website: https://www.gfbr.global/past-meetings/16th-forum-cape-town-south-africa-29-30-november-2022/ ). As opposed to providing a detailed summary in this paper, we aim to briefly highlight central issues raised, solutions proposed, and the challenges facing the research ethics community in the years to come.

In this way, our primary aim in this paper is to present a synthesis of the challenges and opportunities raised at the GFBR meeting and in the planning process, followed by our reflections as a group of authors on their significance for governance leaders in the coming years. We acknowledge that the views represented at the meeting and in our results are a partial representation of the universe of views on this topic; however, the GFBR leadership invested a great deal of resources in convening a deeply diverse and thoughtful group of researchers and practitioners working on themes of bioethics related to AI for global health including those based in LMICs. We contend that it remains rare to convene such a strong group for an extended time and believe that many of the challenges and opportunities raised demand attention for more ethical futures of AI for health. Nonetheless, our results are primarily descriptive and are thus not explicitly grounded in a normative argument. We make effort in the Discussion section to contextualize our results by describing their significance and connecting them to broader efforts to reform global health research and practice.

Uniquely important ethical issues for AI in global health research

Presentations and group dialogue over the course of the forum raised several issues for consideration, and here we describe four overarching themes for the ethical governance of AI in global health research. Brief descriptions of each issue can be found in Table  1 . Reports referred to throughout the paper are available at the GFBR website provided above.

The first overarching thematic issue relates to the appropriateness of building AI technologies in response to health-related challenges in the first place. Case study presentations referred to initiatives where AI technologies were highly appropriate, such as in ear shape biometric identification to more accurately link electronic health care records to individual patients in Zambia (Alinani Simukanga). Although important ethical issues were raised with respect to privacy, trust, and community engagement in this initiative, the AI-based solution was appropriately matched to the challenge of accurately linking electronic records to specific patient identities. In contrast, forum participants raised questions about the appropriateness of an initiative using AI to improve the quality of handwashing practices in an acute care hospital in India (Niyoshi Shah), which led to gaming the algorithm. Overall, participants acknowledged the dangers of techno-solutionism, in which AI researchers and developers treat AI technologies as the most obvious solutions to problems that in actuality demand much more complex strategies to address [ 24 ]. However, forum participants agreed that RECs in different contexts have differing degrees of power to raise issues of the appropriateness of an AI-based intervention.

The second overarching thematic issue related to whether and how AI-based systems transfer from one national health context to another. One central issue raised by a number of case study presentations related to the challenges of validating an algorithm with data collected in a local environment. For example, one case study presentation described a project that would involve the collection of personally identifiable data for sensitive group identities, such as tribe, clan, or religion, in the jurisdictions involved (South Africa, Nigeria, Tanzania, Uganda and the US; Gakii Masunga). Doing so would enable the team to ensure that those groups were adequately represented in the dataset to ensure the resulting algorithm was not biased against specific community groups when deployed in that context. However, some members of these communities might desire to be represented in the dataset, whereas others might not, illustrating the need to balance autonomy and inclusivity. It was also widely recognized that collecting these data is an immense challenge, particularly when historically oppressive practices have led to a low-trust environment for international organizations and the technologies they produce. It is important to note that in some countries such as South Africa and Rwanda, it is illegal to collect information such as race and tribal identities, re-emphasizing the importance for cultural awareness and avoiding “one size fits all” solutions.

The third overarching thematic issue is related to understanding accountabilities for both the impacts of AI technologies and governance decision-making regarding their use. Where global health research involving AI leads to longer-term harms that might fall outside the usual scope of issues considered by a REC, who is to be held accountable, and how? This question was raised as one that requires much further attention, with law being mixed internationally regarding the mechanisms available to hold researchers, innovators, and their institutions accountable over the longer term. However, it was recognized in breakout group discussion that many jurisdictions are developing strong data protection regimes related specifically to international collaboration for research involving health data. For example, Kenya’s Data Protection Act requires that any internationally funded projects have a local principal investigator who will hold accountability for how data are shared and used [ 25 ]. The issue of research partnerships with commercial entities was raised by many participants in the context of accountability, pointing toward the urgent need for clear principles related to strategies for engagement with commercial technology companies in global health research.

The fourth and final overarching thematic issue raised here is that of consent. The issue of consent was framed by the widely shared recognition that models of individual, explicit consent might not produce a supportive environment for AI innovation that relies on the secondary uses of health-related datasets to build AI algorithms. Given this recognition, approaches such as community oversight of health data uses were suggested as a potential solution. However, the details of implementing such community oversight mechanisms require much further attention, particularly given the unique perspectives on health data in different country settings in global health research. Furthermore, some uses of health data do continue to require consent. One case study of South Africa, Nigeria, Kenya, Ethiopia and Uganda suggested that when health data are shared across borders, individual consent remains necessary when data is transferred from certain countries (Nezerith Cengiz). Broader clarity is necessary to support the ethical governance of health data uses for AI in global health research.

Recommendations for ethical governance of AI in global health research

Dialogue at the forum led to a range of suggestions for promoting ethical conduct of AI research for global health, related to the various roles of actors involved in the governance of AI research broadly defined. The strategies are written for actors we refer to as “governance leaders”, those people distributed throughout the AI for global health research ecosystem who are responsible for ensuring the ethical and socially responsible conduct of global health research involving AI (including researchers themselves). These include RECs, government regulators, health care leaders, health professionals, corporate social accountability officers, and others. Enacting these strategies would bolster the ethical governance of AI for global health more generally, enabling multiple actors to fulfill their roles related to governing research and development activities carried out across multiple organizations, including universities, academic health sciences centers, start-ups, and technology corporations. Specific suggestions are summarized in Table  2 .

First, forum participants suggested that governance leaders including RECs, should remain up to date on recent advances in the regulation of AI for health. Regulation of AI for health advances rapidly and takes on different forms in jurisdictions around the world. RECs play an important role in governance, but only a partial role; it was deemed important for RECs to acknowledge how they fit within a broader governance ecosystem in order to more effectively address the issues within their scope. Not only RECs but organizational leaders responsible for procurement, researchers, and commercial actors should all commit to efforts to remain up to date about the relevant approaches to regulating AI for health care and public health in jurisdictions internationally. In this way, governance can more adequately remain up to date with advances in regulation.

Second, forum participants suggested that governance leaders should focus on ethical governance of health data as a basis for ethical global health AI research. Health data are considered the foundation of AI development, being used to train AI algorithms for various uses [ 26 ]. By focusing on ethical governance of health data generation, sharing, and use, multiple actors will help to build an ethical foundation for AI development among global health researchers.

Third, forum participants believed that governance processes should incorporate AI impact assessments where appropriate. An AI impact assessment is the process of evaluating the potential effects, both positive and negative, of implementing an AI algorithm on individuals, society, and various stakeholders, generally over time frames specified in advance of implementation [ 27 ]. Although not all types of AI research in global health would warrant an AI impact assessment, this is especially relevant for those studies aiming to implement an AI system for intervention into health care or public health. Organizations such as RECs can use AI impact assessments to boost understanding of potential harms at the outset of a research project, encouraging researchers to more deeply consider potential harms in the development of their study.

Fourth, forum participants suggested that governance decisions should incorporate the use of environmental impact assessments, or at least the incorporation of environment values when assessing the potential impact of an AI system. An environmental impact assessment involves evaluating and anticipating the potential environmental effects of a proposed project to inform ethical decision-making that supports sustainability [ 28 ]. Although a relatively new consideration in research ethics conversations [ 29 ], the environmental impact of building technologies is a crucial consideration for the public health commitment to environmental sustainability. Governance leaders can use environmental impact assessments to boost understanding of potential environmental harms linked to AI research projects in global health over both the shorter and longer terms.

Fifth, forum participants suggested that governance leaders should require stronger transparency in the development of AI algorithms in global health research. Transparency was considered essential in the design and development of AI algorithms for global health to ensure ethical and accountable decision-making throughout the process. Furthermore, whether and how researchers have considered the unique contexts into which such algorithms may be deployed can be surfaced through stronger transparency, for example in describing what primary considerations were made at the outset of the project and which stakeholders were consulted along the way. Sharing information about data provenance and methods used in AI development will also enhance the trustworthiness of the AI-based research process.

Sixth, forum participants suggested that governance leaders can encourage or require community engagement at various points throughout an AI project. It was considered that engaging patients and communities is crucial in AI algorithm development to ensure that the technology aligns with community needs and values. However, participants acknowledged that this is not a straightforward process. Effective community engagement requires lengthy commitments to meeting with and hearing from diverse communities in a given setting, and demands a particular set of skills in communication and dialogue that are not possessed by all researchers. Encouraging AI researchers to begin this process early and build long-term partnerships with community members is a promising strategy to deepen community engagement in AI research for global health. One notable recommendation was that research funders have an opportunity to incentivize and enable community engagement with funds dedicated to these activities in AI research in global health.

Seventh, forum participants suggested that governance leaders can encourage researchers to build strong, fair partnerships between institutions and individuals across country settings. In a context of longstanding imbalances in geopolitical and economic power, fair partnerships in global health demand a priori commitments to share benefits related to advances in medical technologies, knowledge, and financial gains. Although enforcement of this point might be beyond the remit of RECs, commentary will encourage researchers to consider stronger, fairer partnerships in global health in the longer term.

Eighth, it became evident that it is necessary to explore new forms of regulatory experimentation given the complexity of regulating a technology of this nature. In addition, the health sector has a series of particularities that make it especially complicated to generate rules that have not been previously tested. Several participants highlighted the desire to promote spaces for experimentation such as regulatory sandboxes or innovation hubs in health. These spaces can have several benefits for addressing issues surrounding the regulation of AI in the health sector, such as: (i) increasing the capacities and knowledge of health authorities about this technology; (ii) identifying the major problems surrounding AI regulation in the health sector; (iii) establishing possibilities for exchange and learning with other authorities; (iv) promoting innovation and entrepreneurship in AI in health; and (vi) identifying the need to regulate AI in this sector and update other existing regulations.

Ninth and finally, forum participants believed that the capabilities of governance leaders need to evolve to better incorporate expertise related to AI in ways that make sense within a given jurisdiction. With respect to RECs, for example, it might not make sense for every REC to recruit a member with expertise in AI methods. Rather, it will make more sense in some jurisdictions to consult with members of the scientific community with expertise in AI when research protocols are submitted that demand such expertise. Furthermore, RECs and other approaches to research governance in jurisdictions around the world will need to evolve in order to adopt the suggestions outlined above, developing processes that apply specifically to the ethical governance of research using AI methods in global health.

Research involving the development and implementation of AI technologies continues to grow in global health, posing important challenges for ethical governance of AI in global health research around the world. In this paper we have summarized insights from the 2022 GFBR, focused specifically on issues in research ethics related to AI for global health research. We summarized four thematic challenges for governance related to AI in global health research and nine suggestions arising from presentations and dialogue at the forum. In this brief discussion section, we present an overarching observation about power imbalances that frames efforts to evolve the role of governance in global health research, and then outline two important opportunity areas as the field develops to meet the challenges of AI in global health research.

Dialogue about power is not unfamiliar in global health, especially given recent contributions exploring what it would mean to de-colonize global health research, funding, and practice [ 30 , 31 ]. Discussions of research ethics applied to AI research in global health contexts are deeply infused with power imbalances. The existing context of global health is one in which high-income countries primarily located in the “Global North” charitably invest in projects taking place primarily in the “Global South” while recouping knowledge, financial, and reputational benefits [ 32 ]. With respect to AI development in particular, recent examples of digital colonialism frame dialogue about global partnerships, raising attention to the role of large commercial entities and global financial capitalism in global health research [ 21 , 22 ]. Furthermore, the power of governance organizations such as RECs to intervene in the process of AI research in global health varies widely around the world, depending on the authorities assigned to them by domestic research governance policies. These observations frame the challenges outlined in our paper, highlighting the difficulties associated with making meaningful change in this field.

Despite these overarching challenges of the global health research context, there are clear strategies for progress in this domain. Firstly, AI innovation is rapidly evolving, which means approaches to the governance of AI for health are rapidly evolving too. Such rapid evolution presents an important opportunity for governance leaders to clarify their vision and influence over AI innovation in global health research, boosting the expertise, structure, and functionality required to meet the demands of research involving AI. Secondly, the research ethics community has strong international ties, linked to a global scholarly community that is committed to sharing insights and best practices around the world. This global community can be leveraged to coordinate efforts to produce advances in the capabilities and authorities of governance leaders to meaningfully govern AI research for global health given the challenges summarized in our paper.

Limitations

Our paper includes two specific limitations that we address explicitly here. First, it is still early in the lifetime of the development of applications of AI for use in global health, and as such, the global community has had limited opportunity to learn from experience. For example, there were many fewer case studies, which detail experiences with the actual implementation of an AI technology, submitted to GFBR 2022 for consideration than was expected. In contrast, there were many more governance reports submitted, which detail the processes and outputs of governance processes that anticipate the development and dissemination of AI technologies. This observation represents both a success and a challenge. It is a success that so many groups are engaging in anticipatory governance of AI technologies, exploring evidence of their likely impacts and governing technologies in novel and well-designed ways. It is a challenge that there is little experience to build upon of the successful implementation of AI technologies in ways that have limited harms while promoting innovation. Further experience with AI technologies in global health will contribute to revising and enhancing the challenges and recommendations we have outlined in our paper.

Second, global trends in the politics and economics of AI technologies are evolving rapidly. Although some nations are advancing detailed policy approaches to regulating AI more generally, including for uses in health care and public health, the impacts of corporate investments in AI and political responses related to governance remain to be seen. The excitement around large language models (LLMs) and large multimodal models (LMMs) has drawn deeper attention to the challenges of regulating AI in any general sense, opening dialogue about health sector-specific regulations. The direction of this global dialogue, strongly linked to high-profile corporate actors and multi-national governance institutions, will strongly influence the development of boundaries around what is possible for the ethical governance of AI for global health. We have written this paper at a point when these developments are proceeding rapidly, and as such, we acknowledge that our recommendations will need updating as the broader field evolves.

Ultimately, coordination and collaboration between many stakeholders in the research ethics ecosystem will be necessary to strengthen the ethical governance of AI in global health research. The 2022 GFBR illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research.

Data availability

All data and materials analyzed to produce this paper are available on the GFBR website: https://www.gfbr.global/past-meetings/16th-forum-cape-town-south-africa-29-30-november-2022/ .

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Acknowledgements

We would like to acknowledge the outstanding contributions of the attendees of GFBR 2022 in Cape Town, South Africa. This paper is authored by members of the GFBR 2022 Planning Committee. We would like to acknowledge additional members Tamra Lysaght, National University of Singapore, and Niresh Bhagwandin, South African Medical Research Council, for their input during the planning stages and as reviewers of the applications to attend the Forum.

This work was supported by Wellcome [222525/Z/21/Z], the US National Institutes of Health, the UK Medical Research Council (part of UK Research and Innovation), and the South African Medical Research Council through funding to the Global Forum on Bioethics in Research.

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Shaw, J., Ali, J., Atuire, C.A. et al. Research ethics and artificial intelligence for global health: perspectives from the global forum on bioethics in research. BMC Med Ethics 25 , 46 (2024). https://doi.org/10.1186/s12910-024-01044-w

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In today’s world, healthcare has become disconnected. Johnson & Johnson is reconnecting the meaning of those two simple words: Health&Care. The goal is to bring together innovations in medicine and technology and the care the company delivers to impact health for humanity. 

Here are 10 recent examples that demonstrate the company’s work in solving the world’s toughest healthcare challenges to transform the patient experience.

Bladder cancer rises from the bladder lining, which makes it challenging to physically access treatments because liquids don’t stay in the bladder for long. To address this, Johnson & Johnson researchers are looking at an investigational novel targeted releasing system that’s inserted into the bladder and steadily delivers therapy directly through this device technology. The targeted release system received  Breakthrough Therapy Designation  from the U.S. Food and Drug Administration (FDA) in 2023.

Inventors and mentors, scientists and lifesavers, nurses exemplify the best of Health&Care. Johnson & Johnson honors their invaluable contributions, providing resources and expanding opportunities for these frontline healthcare professionals. Follow along with one such trailblazer on a typical workday.

Johnson & Johnson has a long history of tackling cardiovascular disease. With its planned acquisition of Shockwave Medical , Johnson & Johnson is further expanding its portfolio into coronary artery disease and peripheral artery disease to ultimately transform the future of cardiovascular treatment. This acquisition follows that of Abiomed, which has accelerated options for people with heart failure .

Cardiac ablation  is a minimally invasive surgical procedure that uses heat or cold energy to destroy a small area of heart tissue that is causing an arrhythmia—which may be the result of atrial fibrillation (AFib) or another cardiac condition. Currently in clinical development at Biosense Webster is Pulsed Field Ablation (PFA) technology, which uses a controlled electric field to ablate cardiac tissue. PFA has the potential to offer safe, consistent and efficient therapy for patients. Among the potential benefits for both patients and physicians: PFA shaves time off the procedure and potentially reduces the risk of damage to the esophagus, veins and nerves that exists with other ablation techniques.

Company researchers are also investigating early interventions in preventing the rare blood cancer  multiple myeloma . The research focuses on smoldering myeloma, a precancerous condition that affects a small portion of people with multiple myeloma. Scientists are investigating whether certain genetic markers can signal which patients with smoldering myeloma are at the highest risk of progressing to multiple myeloma—and if certain treatments may benefit patients with the precancerous condition. 

Black men are two times more likely to die from prostate cancer than most other men, and they’re less likely than white men to receive prostate cancer screening. That’s why Johnson & Johnson launched Talk That Talk TM in 2022. This educational campaign aims to inspire Black men to normalize prostate health discussions and encourage early detection.

A surgical stapler, called the  Echelon™ 3000 , developed by Ethicon, a Johnson & Johnson MedTech company, makes it easier for surgeons to maneuver in tight spaces with more precision. Plus, its ergonomic design means surgeons with different hand sizes can use it effectively. Another feature: It can be operated with one hand at the push of a button. Approved by the FDA in 2022, the Echelon 3000 can be used in multiple minimally invasive procedures, including  thoracic ,  colorectal  and bariatric surgeries.

To help increase the number of medical doctors from diverse communities,  Johnson & Johnson has collaborated with several organizations that aim to promote equity in three areas of medicine that have considerable gaps in representation: optometry ,  surgery  and  clinical trials . Why is this important? Studies show that patients  prefer doctors who share their same race or ethnicity . And when patients from underserved communities are treated by people who share their background, they have better health outcomes, including improved  medication adherence  and  confidence in recommended treatments . 

Johnson & Johnson MedTech has developed the  Monarch™ Platform  for Bronchoscopy, which allows physicians to examine hard-to-reach areas of the lung where conventional bronchoscopes have greater challenges with access and stability. This reach and access to the periphery could potentially extend beyond diagnostic procedures to find lung cancer at an earlier stage. Researchers are now studying how minimally invasive procedures can potentially aid in lung cancer treatment. 

In 2022, the INHANCE™ Shoulder System received FDA clearance to be used in reverse shoulder replacement procedures. The Inhance Shoulder System had previously been approved for use in traditional shoulder replacement surgeries. Using the system involves fewer instruments and fewer surgical steps in the operating room, and it can offer a faster healing time for patients.

In 2023, Johnson & Johnson’s Our Race to Health Equity ( ORTHE ) initiative invested in two pilot programs that encourage and mentor nursing students of color—one in partnership with the National League for Nursing, the other in collaboration with the American Association of Colleges of Nursing. Johnson & Johnson has long been committed to supporting nurses, and that includes finding solutions that boost diversity across the nursing profession.

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Generative A.I. Arrives in the Gene Editing World of CRISPR

Much as ChatGPT generates poetry, a new A.I. system devises blueprints for microscopic mechanisms that can edit your DNA.

The physical structure of OpenCRISPR-1, a gene editor created by A.I. technology from Profluent. Credit... Video by Profluent Bio

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By Cade Metz

Has reported on the intersection of A.I. and health care for a decade.

• April 22, 2024

Generative A.I. technologies can write poetry and computer programs or create images of teddy bears and videos of cartoon characters that look like something from a Hollywood movie.

Now, new A.I. technology is generating blueprints for microscopic biological mechanisms that can edit your DNA, pointing to a future when scientists can battle illness and diseases with even greater precision and speed than they can today.

Described in a research paper published on Monday by a Berkeley, Calif., startup called Profluent, the technology is based on the same methods that drive ChatGPT, the online chatbot that launched the A.I. boom after its release in 2022 . The company is expected to present the paper next month at the annual meeting of the American Society of Gene and Cell Therapy.

Much as ChatGPT learns to generate language by analyzing Wikipedia articles, books and chat logs, Profluent’s technology creates new gene editors after analyzing enormous amounts of biological data, including microscopic mechanisms that scientists already use to edit human DNA.

These gene editors are based on Nobel Prize-winning methods involving biological mechanisms called CRISPR. Technology based on CRISPR is already changing how scientists study and fight illness and disease , providing a way of altering genes that cause hereditary conditions, such as sickle cell anemia and blindness.

Previously, CRISPR methods used mechanisms found in nature — biological material gleaned from bacteria that allows these microscopic organisms to fight off germs.

“They have never existed on Earth,” said James Fraser, a professor and chair of the department of bioengineering and therapeutic sciences at the University of California, San Francisco, who has read Profluent’s research paper. “The system has learned from nature to create them, but they are new.”

The hope is that the technology will eventually produce gene editors that are more nimble and more powerful than those that have been honed over billions of years of evolution.

On Monday, Profluent also said that it had used one of these A.I.-generated gene editors to edit human DNA and that it was “open sourcing” this editor, called OpenCRISPR-1. That means it is allowing individuals, academic labs and companies to experiment with the technology for free.

A.I. researchers often open source the underlying software that drives their A.I. systems , because it allows others to build on their work and accelerate the development of new technologies. But it is less common for biological labs and pharmaceutical companies to open source inventions like OpenCRISPR-1.

Though Profluent is open sourcing the gene editors generated by its A.I. technology, it is not open sourcing the A.I. technology itself.

The project is part of a wider effort to build A.I. technologies that can improve medical care. Scientists at the University of Washington, for instance, are using the methods behind chatbots like OpenAI’s ChatGPT and image generators like Midjourney to create entirely new proteins — the microscopic molecules that drive all human life — as they work to accelerate the development of new vaccines and medicines.

(The New York Times has sued OpenAI and its partner, Microsoft, on claims of copyright infringement involving artificial intelligence systems that generate text.)

Generative A.I. technologies are driven by what scientists call a neural network , a mathematical system that learns skills by analyzing vast amounts of data. The image creator Midjourney, for example, is underpinned by a neural network that has analyzed millions of digital images and the captions that describe each of those images. The system learned to recognize the links between the images and the words. So when you ask it for an image of a rhinoceros leaping off the Golden Gate Bridge, it knows what to do.

Profluent’s technology is driven by a similar A.I. model that learns from sequences of amino acids and nucleic acids — the chemical compounds that define the microscopic biological mechanisms that scientists use to edit genes. Essentially, it analyzes the behavior of CRISPR gene editors pulled from nature and learns how to generate entirely new gene editors.

“These A.I. models learn from sequences — whether those are sequences of characters or words or computer code or amino acids,” said Profluent’s chief executive, Ali Madani, a researcher who previously worked in the A.I. lab at the software giant Salesforce.

Profluent has not yet put these synthetic gene editors through clinical trials, so it is not clear if they can match or exceed the performance of CRISPR. But this proof of concept shows that A.I. models can produce something capable of editing the human genome.

Still, it is unlikely to affect health care in the short term. Fyodor Urnov, a gene editing pioneer and scientific director at the Innovative Genomics Institute at the University of California, Berkeley, said scientists had no shortage of naturally occurring gene editors that they could use to fight illness and disease. The bottleneck, he said, is the cost of pushing these editors through preclinical studies, such as safety, manufacturing and regulatory reviews, before they can be used on patients.

But generative A.I. systems often hold enormous potential because they tend to improve quickly as they learn from increasingly large amounts of data. If technology like Profluent’s continues to improve, it could eventually allow scientists to edit genes in far more precise ways. The hope, Dr. Urnov said, is that this could, in the long term, lead to a world where medicines and treatments are quickly tailored to individual people even faster than we can do today.

“I dream of a world where we have CRISPR on demand within weeks,” he said.

Scientists have long cautioned against using CRISPR for human enhancement because it is a relatively new technology that could potentially have undesired side effects, such as triggering cancer, and have warned against unethical uses, such as genetically modifying human embryos.

This is also a concern with synthetic gene editors. But scientists already have access to everything they need to edit embryos.

“A bad actor, someone who is unethical, is not worried about whether they use an A.I.-created editor or not,” Dr. Fraser said. “They are just going to go ahead and use what’s available.”

Cade Metz writes about artificial intelligence, driverless cars, robotics, virtual reality and other emerging areas of technology. More about Cade Metz

Explore Our Coverage of Artificial Intelligence

News  and Analysis

Eight daily newspapers owned by Alden Global Capital sued OpenAI and Microsoft , accusing the tech companies of illegally using news articles to power their A.I. chatbots.

The spending that the tech industry’s giants expect A.I. to require, for the chips and data centers , is starting to come into focus — and it is jarringly large.

The table stakes for A.I. start-ups to compete with the likes of Microsoft and Google are in the billions of dollars. And even that may not be enough .

The Age of A.I.

A new category of apps promises to relieve parents of drudgery, with an assist from A.I . But a family’s grunt work is more human, and valuable, than it seems.

Despite Mark Zuckerberg’s hope for Meta’s A.I. assistant to be the smartest , it struggles with facts, numbers and web search.

Much as ChatGPT generates poetry, a new A.I. system devises blueprints for microscopic mechanisms  that can edit your DNA.

Could A.I. change India’s elections? Avatars are addressing voters by name, in whichever of India’s many languages they speak. Experts see potential for misuse  in a country already rife with disinformation.

Which A.I. system writes the best computer code or generates the most realistic image? Right now, there’s no easy way to answer those questions, our technology columnist writes .

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40 Facts About Elektrostal

Written by Lanette Mayes

Modified & Updated: 02 Mar 2024

Reviewed by Jessica Corbett

Elektrostal is a vibrant city located in the Moscow Oblast region of Russia. With a rich history, stunning architecture, and a thriving community, Elektrostal is a city that has much to offer. Whether you are a history buff, nature enthusiast, or simply curious about different cultures, Elektrostal is sure to captivate you.

This article will provide you with 40 fascinating facts about Elektrostal, giving you a better understanding of why this city is worth exploring. From its origins as an industrial hub to its modern-day charm, we will delve into the various aspects that make Elektrostal a unique and must-visit destination.

So, join us as we uncover the hidden treasures of Elektrostal and discover what makes this city a true gem in the heart of Russia.

Key Takeaways:

• Elektrostal, known as the “Motor City of Russia,” is a vibrant and growing city with a rich industrial history, offering diverse cultural experiences and a strong commitment to environmental sustainability.
• With its convenient location near Moscow, Elektrostal provides a picturesque landscape, vibrant nightlife, and a range of recreational activities, making it an ideal destination for residents and visitors alike.

Known as the “Motor City of Russia.”

Elektrostal, a city located in the Moscow Oblast region of Russia, earned the nickname “Motor City” due to its significant involvement in the automotive industry.

Home to the Elektrostal Metallurgical Plant.

Elektrostal is renowned for its metallurgical plant, which has been producing high-quality steel and alloys since its establishment in 1916.

Boasts a rich industrial heritage.

Elektrostal has a long history of industrial development, contributing to the growth and progress of the region.

Founded in 1916.

The city of Elektrostal was founded in 1916 as a result of the construction of the Elektrostal Metallurgical Plant.

Located approximately 50 kilometers east of Moscow.

Elektrostal is situated in close proximity to the Russian capital, making it easily accessible for both residents and visitors.

Known for its vibrant cultural scene.

Elektrostal is home to several cultural institutions, including museums, theaters, and art galleries that showcase the city’s rich artistic heritage.

A popular destination for nature lovers.

Surrounded by picturesque landscapes and forests, Elektrostal offers ample opportunities for outdoor activities such as hiking, camping, and birdwatching.

Hosts the annual Elektrostal City Day celebrations.

Every year, Elektrostal organizes festive events and activities to celebrate its founding, bringing together residents and visitors in a spirit of unity and joy.

Has a population of approximately 160,000 people.

Elektrostal is home to a diverse and vibrant community of around 160,000 residents, contributing to its dynamic atmosphere.

Boasts excellent education facilities.

The city is known for its well-established educational institutions, providing quality education to students of all ages.

A center for scientific research and innovation.

Elektrostal serves as an important hub for scientific research, particularly in the fields of metallurgy, materials science, and engineering.

Surrounded by picturesque lakes.

The city is blessed with numerous beautiful lakes, offering scenic views and recreational opportunities for locals and visitors alike.

Well-connected transportation system.

Elektrostal benefits from an efficient transportation network, including highways, railways, and public transportation options, ensuring convenient travel within and beyond the city.

Famous for its traditional Russian cuisine.

Food enthusiasts can indulge in authentic Russian dishes at numerous restaurants and cafes scattered throughout Elektrostal.

Home to notable architectural landmarks.

Elektrostal boasts impressive architecture, including the Church of the Transfiguration of the Lord and the Elektrostal Palace of Culture.

Offers a wide range of recreational facilities.

Residents and visitors can enjoy various recreational activities, such as sports complexes, swimming pools, and fitness centers, enhancing the overall quality of life.

Provides a high standard of healthcare.

Elektrostal is equipped with modern medical facilities, ensuring residents have access to quality healthcare services.

Home to the Elektrostal History Museum.

The Elektrostal History Museum showcases the city’s fascinating past through exhibitions and displays.

A hub for sports enthusiasts.

Elektrostal is passionate about sports, with numerous stadiums, arenas, and sports clubs offering opportunities for athletes and spectators.

Celebrates diverse cultural festivals.

Throughout the year, Elektrostal hosts a variety of cultural festivals, celebrating different ethnicities, traditions, and art forms.

Electric power played a significant role in its early development.

Elektrostal owes its name and initial growth to the establishment of electric power stations and the utilization of electricity in the industrial sector.

Boasts a thriving economy.

The city’s strong industrial base, coupled with its strategic location near Moscow, has contributed to Elektrostal’s prosperous economic status.

Houses the Elektrostal Drama Theater.

The Elektrostal Drama Theater is a cultural centerpiece, attracting theater enthusiasts from far and wide.

Popular destination for winter sports.

Elektrostal’s proximity to ski resorts and winter sport facilities makes it a favorite destination for skiing, snowboarding, and other winter activities.

Promotes environmental sustainability.

Elektrostal prioritizes environmental protection and sustainability, implementing initiatives to reduce pollution and preserve natural resources.

Home to renowned educational institutions.

Elektrostal is known for its prestigious schools and universities, offering a wide range of academic programs to students.

Committed to cultural preservation.

The city values its cultural heritage and takes active steps to preserve and promote traditional customs, crafts, and arts.

Hosts an annual International Film Festival.

The Elektrostal International Film Festival attracts filmmakers and cinema enthusiasts from around the world, showcasing a diverse range of films.

Encourages entrepreneurship and innovation.

Elektrostal supports aspiring entrepreneurs and fosters a culture of innovation, providing opportunities for startups and business development.

Offers a range of housing options.

Elektrostal provides diverse housing options, including apartments, houses, and residential complexes, catering to different lifestyles and budgets.

Home to notable sports teams.

Elektrostal is proud of its sports legacy, with several successful sports teams competing at regional and national levels.

Boasts a vibrant nightlife scene.

Residents and visitors can enjoy a lively nightlife in Elektrostal, with numerous bars, clubs, and entertainment venues.

Promotes cultural exchange and international relations.

Elektrostal actively engages in international partnerships, cultural exchanges, and diplomatic collaborations to foster global connections.

Surrounded by beautiful nature reserves.

Nearby nature reserves, such as the Barybino Forest and Luchinskoye Lake, offer opportunities for nature enthusiasts to explore and appreciate the region’s biodiversity.

Commemorates historical events.

The city pays tribute to significant historical events through memorials, monuments, and exhibitions, ensuring the preservation of collective memory.

Promotes sports and youth development.

Elektrostal invests in sports infrastructure and programs to encourage youth participation, health, and physical fitness.

Hosts annual cultural and artistic festivals.

Throughout the year, Elektrostal celebrates its cultural diversity through festivals dedicated to music, dance, art, and theater.

Provides a picturesque landscape for photography enthusiasts.

The city’s scenic beauty, architectural landmarks, and natural surroundings make it a paradise for photographers.

Connects to Moscow via a direct train line.

The convenient train connection between Elektrostal and Moscow makes commuting between the two cities effortless.

A city with a bright future.

Elektrostal continues to grow and develop, aiming to become a model city in terms of infrastructure, sustainability, and quality of life for its residents.

In conclusion, Elektrostal is a fascinating city with a rich history and a vibrant present. From its origins as a center of steel production to its modern-day status as a hub for education and industry, Elektrostal has plenty to offer both residents and visitors. With its beautiful parks, cultural attractions, and proximity to Moscow, there is no shortage of things to see and do in this dynamic city. Whether you’re interested in exploring its historical landmarks, enjoying outdoor activities, or immersing yourself in the local culture, Elektrostal has something for everyone. So, next time you find yourself in the Moscow region, don’t miss the opportunity to discover the hidden gems of Elektrostal.

Q: What is the population of Elektrostal?

A: As of the latest data, the population of Elektrostal is approximately XXXX.

Q: How far is Elektrostal from Moscow?

A: Elektrostal is located approximately XX kilometers away from Moscow.

Q: Are there any famous landmarks in Elektrostal?

A: Yes, Elektrostal is home to several notable landmarks, including XXXX and XXXX.

Q: What industries are prominent in Elektrostal?

A: Elektrostal is known for its steel production industry and is also a center for engineering and manufacturing.

Q: Are there any universities or educational institutions in Elektrostal?

A: Yes, Elektrostal is home to XXXX University and several other educational institutions.

Q: What are some popular outdoor activities in Elektrostal?

A: Elektrostal offers several outdoor activities, such as hiking, cycling, and picnicking in its beautiful parks.

Q: Is Elektrostal well-connected in terms of transportation?

A: Yes, Elektrostal has good transportation links, including trains and buses, making it easily accessible from nearby cities.

Q: Are there any annual events or festivals in Elektrostal?

A: Yes, Elektrostal hosts various events and festivals throughout the year, including XXXX and XXXX.

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