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Journal of International Dental and Medical Research

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IOSR Journal of Dental and Medical Sciences is one of the speciality Journal in Dental Science and Medical Science published by International Organization of Scientific Research (IOSR). The Journal publishes papers of the highest scientific merit and widest possible scope work in all areas related to medical and dental science. The Journal welcome review articles, leading medical and clinical research articles, technical notes, case reports and others.

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Journal of Advanced Medical and Dental Sciences Research (JAMDSR) is an official publication of “Society for Scientific Research and Studies” registered under the Society Registration act, 1860 (Act XXI of 1860). It is an open access, multidisciplinary peer reviewed monthly journal to provide a forum for the presentation and criticism of original, innovative and thought provocative ideas in medical and dental sciences. All articles have to be original articles that have not been published elsewhere or are being considered for publication in other journals. All articles submitted will be peer reviewed by experts. Every effort will be made to complete the review process within 2 weeks and communicated to the corresponding author. Papers should be submitted directly to E-Mail - [email protected] along with covering letter and copyright form. JAMDSR confers revised MCI guidelines with regards to research publication in the matter of promotions of teaching faculty in Medical colleges.

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Journal of Medical and Dental Science Research ISSN (o) : 2394 - 076X   ISSN (p) : 2394 - 0791

Journal of medical and dental science research (jmdsr).

Journal of Medical and Dental Science Research is an international peer-reviewed online & print Open access journal published Monthly . It publishes the results of original research in the field of Dental and Medical. The Journal publishes papers of the highest scientific merit and widest possible scope work in all areas related to medical and dental science. The Journal welcome review articles, leading medical and clinical research articles, technical notes, case reports and others. We accept for publication manuscripts that were not published earlier in any form.

Our objective is to encourage and provide international publication for researchers, scientists and engineers. We welcome original un-published researches, articles, surveys and review papers from all over the world.

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Journal of Medical and Dental Science Research (JMDSR) publishes scholarly research, review and survey articles on any aspect of computer science. This Journal topics include, but are not limited to:

• Oral & Maxillofacial Surgery
• Implantology/Dentoaveolar Surgery
• Microbiology
• Oral & Maxillofacial Radiology
• Emerging technologies
• Periodontics
• Pedodontics
• Forensic Medicine
• Preventive and community dentistry
• Clinical Biochemistry
• Prosthodontics
• Pharmaceutical Biotechnology
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• Pharmaceutical Microbiology
• Oral & Maxillofacial Pathology
• Medical Informatics
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• Medicine & Rheumatolohy
• Endodontics
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Linking ISSN (ISSN-L): 1309-100X

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Title proper: Journal of international dental and medical research.

Abbreviated key-title: J. int. dent. med. res.

Other variant title: JIDMR

Original alphabet of title: Basic roman

Publisher: Diyarbakir: Ektodermal Displazi Grubu-Türkiye

Dates of publication: 2008- 9999

Frequency: Three times a year

Type of resource: Periodical

Language: English

Country: Turkey

Medium: Online

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Continues: International dental and medical disorders, 1308-1322

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Record creation date: 12/11/2013

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International Journal Dental and Medical Sciences   Research  is one of the specialty Journal in Dental Science and Medical Science published by  International Journal Dental and Medical Sciences   Research  ( IJDMSR ).  The Journal publishes papers of the highest scientific merit and widest possible scope work in all areas related to dental and  medical science. The Journal welcome review articles, leading medical and clinical research articles, technical notes, case reports and others.

International Journal Dental and Medical Sciences   Research  is an international peer-reviewed online Open access journal published Monthly . It publishes the results of original research in the field of Dental and Medical.

The Journal welcome review articles, leading medical and clinical research articles, technical notes, case reports and others. We accept for publication manuscripts that were not published earlier in any form.

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Quantum mechanical analysis of yttrium-stabilized zirconia and alumina: implications for mechanical performance of esthetic crowns

• Ravinder S. Saini 1 ,
• Abdulkhaliq Ali F. Alshadidi 1 ,
• Vishwanath Gurumurthy 1 ,
• Abdulmajeed Okshah 1 ,
• Sunil Kumar Vaddamanu 1 ,
• Rayan Ibrahim H. Binduhayyim 1 ,
• Saurabh Chaturvedi 2 ,
• Shashit Shetty Bavabeedu 3 &
• Artak Heboyan 4 , 5 , 6  

European Journal of Medical Research volume  29 , Article number:  254 ( 2024 ) Cite this article

Metrics details

Yttrium-stabilized zirconia (YSZ) and alumina are the most commonly used dental esthetic crown materials. This study aimed to provide detailed information on the comparison between yttrium-stabilized zirconia (YSZ) and alumina, the two materials most often used for esthetic crowns in dentistry.

Methodology

The ground-state energy of the materials was calculated using the Cambridge Serial Total Energy Package (CASTEP) code, which employs a first-principles method based on density functional theory (DFT). The electronic exchange–correlation energy was evaluated using the generalized gradient approximation (GGA) within the Perdew (Burke) Ernzerhof scheme.

Optimization of the geometries and investigation of the optical properties, dynamic stability, band structures, refractive indices, and mechanical properties of these materials contribute to a holistic understanding of these materials. Geometric optimization of YSZ provides important insights into its dynamic stability based on observations of its crystal structure and polyhedral geometry, which show stable configurations. Alumina exhibits a distinctive charge, kinetic, and potential (CKP) geometry, which contributes to its interesting structural framework and molecular-level stability. The optical properties of alumina were evaluated using pseudo-atomic computations, demonstrating its responsiveness to external stimuli. The refractive indices, reflectance, and dielectric functions indicate that the transmission of light by alumina depends on numerous factors that are essential for the optical performance of alumina as a material for esthetic crowns. The band structures of both the materials were explored, and the band gap of alumina was determined to be 5.853 eV. In addition, the band structure describes electronic transitions that influence the conductivity and optical properties of a material. The stability of alumina can be deduced from its bandgap, an essential property that determines its use as a dental material. Refractive indices are vital optical properties of esthetic crown materials. Therefore, the ability to understand their refractive-index graphs explains their transparency and color distortion through how the material responds to light..The regulated absorption characteristics exhibited by YSZ render it a highly attractive option for the development of esthetic crowns, as it guarantees minimal color distortion.

The acceptability of materials for esthetic crowns is strongly determined by mechanical properties such as elastic stiffness constants, Young's modulus, and shear modulus. YSZ is a highly durable material for dental applications, owing to its superior mechanical strength.

Introduction

An esthetic dental crown is an esthetic restoration used to replace the original shape, color, size, and thickness of teeth that are damaged or weakened [ 1 ]. This dental procedure is routinely used when a tooth has extensive decay coupled with structural damage, or when the tooth lacks a cosmetically acceptable appearance [ 2 ]. The principal aim of an esthetic crown is to safeguard the damaged tooth while simultaneously improving its function and esthetics [ 3 ].

The materials used to make esthetic crowns are different, and the choice depends on the location of the tooth, chewing needs, and patient preference [ 4 , 5 ]. Porcelain or ceramic crowns are natural-looking teeth that are especially suitable for the front teeth or areas of the mouth where the teeth are visible [ 6 ]. Porcelain-fused-to-metal crowns combine the esthetic appeal of porcelain with the added strength derived from a metal substructure. Zirconia crowns have become increasingly popular because of their strength and appearance. Zirconia ceramics can withstand chipping and cracking. It can be used in anterior and posterior crowns [ 7 , 8 ]. For cases in which both esthetics and strength are important, the solution is porcelain-fused-to-zirconia crowns, which combine the esthetics of porcelain with the strength of zirconia and can also be used for posterior teeth [ 9 ].

The advantages of metal crowns (made from gold or metal alloys) are their strength and durability [ 10 ]. However, because of their metallic appearance, these crowns are less commonly used in visible areas of the mouth. Composite resin crowns are made of tooth-colored filling materials that can be used to create temporary crowns [ 11 ]. Although less durable compared to some materials, composite resin crowns offer an esthetically pleasing alternative [ 12 , 13 ].

The choice of crown material is based on a concerted decision between the dentist and patient, considering oral health, specific tooth requirements, and personal esthetic preferences [ 14 ]. This approach allows the operator to customize the treatment for each individual patient so that the selected crown material is tailored to their own individual requirements and contributes to the functional and esthetic requirements [ 4 ]. Yttrium-stabilized zirconia (YSZ) and alumina are two types of ceramics that are frequently used to make ceramic dental crowns, with their own advantages for application in dentistry [ 15 , 16 ].

The main component of YSZ is zirconium oxide (ZrO2), which is used with yttrium oxide (Y2O3) as the stabilizing agent [ 17 ]. Yttrium is added to prevent the transformation from a tetragonal to monoclinic crystal structure, thus improving its mechanical properties [ 18 ]. YSZ has excellent strength, toughness, and hardness and is a viable material for dental crowns. Its high fracture resistance protects the crown from chipping or cracking and is biocompatible with the oral environment [ 19 ]. In terms of esthetics, YSZ can be matched in color to more natural teeth, and additional translucency adds to the more natural appearance of restorations. It is suitable for both anterior and posterior teeth [ 20 ].

Alumina crowns, in contrast, are largely made of aluminum oxide (aluminum trioxide or Al 6 2O 3 ), which is a ceramic that is well known for its hardness and resistance to wear [ 21 ]. Alumina exhibits notable hardness and wear resistance that contribute to its durability [ 22 , 23 ]. It has excellent biocompatibility with oral tissues and can be made to match the color of natural teeth; while it is less translucent than YSZ, the esthetics of alumina crowns are continuously improved through material processing [ 24 ]. Alumina crowns are commonly used for anterior teeth where esthetics are a primary concern, and they may be chosen for cases where wear resistance is a key consideration [ 25 , 26 ]. Yttrium-stabilized zirconia and alumina are suitable options for the production of esthetic dental crowns. The choice between the two materials depends on the location of the tooth, type of clinical requirement, and patient’s choice [ 27 ]. These ceramics continue to evolve as new advancements in material science become available to the dental profession, which ultimately allows dentists to provide optimized functional and esthetic outcomes in restorative dentistry [ 28 , 29 ].

In this study, we comprehensively analyzed the mechanical properties, Density of states (DOS), integrated DOS, band structures, optical properties, and stress properties of yttrium-stabilized zirconia (YSZ) and alumina, specifically in the context of their application in esthetic dental crowns. The calculations were based on the computational approach of the CASTEP (Cambridge Serial Total Energy Package) code. The results were verified to provide ideas regarding the structural, electronic, and optical parameters of these materials and to identify their potential usefulness in esthetic crown applications.

Material and methodology

The Cambridge Serial Total Energy Package (CASTEP) code [ 30 , 31 ], utilizing a first-principles approach grounded in density functional theory (DFT), was employed to calculate the ground-state energy of the materials. The generalized gradient approximation (GGA) within the Perdew (Burke) Ernzerhof scheme was used to evaluate the electronic exchange–correlation energy. Vanderbilt-type norm-conserving pseudopotentials, along with a Koelling–Harmon relativistic treatment, were applied to represent the interaction between the valence electrons and ion cores. This pseudopotential selection balances the computational efficiency with the accuracy [ 32 , 33 ]. The valence electron configurations considered were 1s 2 2s 2 2p 4 for 0, 1s 2 2s 2 2p 6 3s 2 3p 1 for Al in alumina, and 1s 2 2s 2 2p 6 3s 2 3p 6 3d 10 4s 2 4p 6 4d 1 5s 2 for Y and 1s 2 2s 2 2p 6 3s 2 3p 6 3d 10 4s 2 4p 6 4d 2 5s 2 for zirconia in YSZ.

Geometry optimization for yttrium-stabilized zirconia and alumina was performed using the limited-memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) minimization scheme to achieve the lowest energy structure. A plane-wave cutoff energy of 500 eV for alumina and 625 eV for YSZ was used for the expansion. Brillouin zone (BZ) integration was conducted using the Monkhorst–Pack method, employing the k-point for alumina (3 × 3 × 1) and YSZ (2 × 2 × 2). The geometry optimization employed convergence tolerances of 10 -4  eV/atom for total energy, 10 -2  Å for maximum lattice point displacement, 0.03 eV Å -1 for maximum ionic Hellmann–Feynman force, and 0.05 GPa for maximum stress tolerance. To guarantee accurate structural, elastic, and electronic band structure property estimates while preserving the computational efficiency, finite basis set modifications were used.

Results and discussion

Structural properties.

The structural properties of alumina were determined through a geometry optimization process employing the LBFGS (limited-memory Broyden–Fletcher–Goldfarb–Shanno) minimization scheme [ 34 ]. The optimization involved an unbounded number of LBFGS updates with a preconditioned LBFGS activated using an exponential (EXP) stabilization constant of 0.1000 and a parameter A value of 3.0000. The real lattice parameters were a  = 4.759 Å, b  = 4.759 Å, and c  = 12.991 Å, with corresponding cell angles of α  = 90.000°, β  = 90.000°, and γ  = 120.000°. The current volume of the unit cell was calculated as 254.803051 A 3 , resulting in a density of 2.400943 AMU/A 3 or 3.986860 g/cm 3 . The crystal system was identified as trigonal with a hexagonal geometry. The rhombohedral centers were determined to be at coordinates (0, 0, 0), (2/3, 1/3, 1/3), and (1/3, 2/3, 2/3), corresponding to crystal class – 3 m. Additionally, the LBFGS optimization results indicated a final enthalpy of − 9.29467617 × 10 3  eV, a final frequency of 543.62876 cm -1 , and a final bulk modulus of 220.64766 GPa. These optimization parameters, including the estimated bulk modulus and frequency, are crucial for obtaining the lowest-energy structure of alumina, providing insights into its stable geometric configuration and overall structural characteristics.

The structural properties of yttrium-stabilized zirconia (YSZ) were also investigated through geometry optimization using the LBFGS (limited-memory Broyden–Fletcher–Goldfarb–Shanno) minimization scheme. The optimization process utilized an unbounded number of LBFGS updates with an activated preconditioned LBFGS, employing an exponential (EXP) stabilization constant of 0.1000 and a parameter A value of 3.0000. The nearest-neighbor distance, cutoff distance, and parameter mu were determined automatically, whereas the variable cell method with a fixed basis quality was employed. The optimization comprised a maximum of 2 steps, with an estimated bulk modulus of 500.0 GPa and frequency of 1668 cm -1 . The real lattice parameters for the unit cell were identified as a  = 5.154630 Å, b  = 5.154630 Å, and c  = 5.154630 Å, resulting in a cubic geometry with cell angles of α  = 90.000°, β  = 90.000°, and γ  = 90.000°. The current cell volume was calculated as 136.959604 A 3 , resulting in a density of 30.452467 AMU/A 3 or 50.567510 g/cm 3 . The crystal system was characterized as cubic, the geometry was cubic, and the rhombohedral centers were specified at the coordinates (0,0,0). The crystal class was identified as 1, and the space group as P 1 with space number 1. The LBFGS optimization results indicated a final enthalpy of 4.89620241 × 10 5  eV, an unchanged final frequency value from the initial value, and a final bulk modulus of 117.20470 GPa. These findings offer insight into the stable geometric configuration, crystal structure, and overall structural properties of yttrium-stabilized zirconia.

Figure  1 data offer insights into the geometry (Fig.  1 (a)), polyhedron (Fig.  1 (b)), and charge, kinetic, and potential (CKP) (Fig.  1 (c)) energy of alumina (Al 6 2O 3 ) having aluminum (Al) in the + 3 oxidation state and oxygen (O) in the − 2 oxidation state. In Fig.  1 (a), the unit cell of alumina exhibits the following lattice parameters: a  = 4.759 Å, b  = 4.759 Å, c  = 12.991 Å, with angles α  =  β  = 90° and γ  = 120°. The current cell volume was 254.803051 Å 3 and the density was 2.400943 AMU/A 3 or 3.986860 g/cm 3 . The crystal structure was characterized as a supercell containing three primitive cells.

a Coordination environment, b polyhedron, and c charge, kinetics, and potential (CKP) of alumina

In crystallography, a polyhedron is a three-dimensional geometric shape formed by connecting neighboring atoms around a central atom. As shown in Fig.  1 (b), the crystal structures of alumina and aluminum atoms are typically surrounded by oxygen atoms, forming a coordination polyhedron around each aluminum center. In the polyhedron (Fig.  1 (b)), alumina comprises 30 ions distributed between two species, oxygen (O) and aluminum (Al). The highest number of species was 18. The fractional coordinates of the atoms were specified by detailing their positions within a unit cell. As shown in Fig.  1 (c), the potential energy density is influenced by the arrangement of the charged particles (nuclei and electrons). In alumina, the potential energy density is shaped by electrostatic interactions between the positively charged aluminum ions and negatively charged oxygen ions. The ionic character of Al–O bonds contributes to the potential energy landscape.

The crystal structure of yttrium-stabilized zirconia (YSZ) is described in terms of its unit cell parameters ( a  = 5.154630 Å, b  = 5.154630 Å, c  = 5.154630 Å), as shown in Fig.  2 (a). The angles between the lattice vectors were all 90°( α  =  β  =  γ  = 90°) with the same cubic crystal system geometry. The unit cells contained oxygen (O), yttrium (Y), and zirconium (Zr). In Fig.  2 (b), the polyhedron, in the context of crystallography, typically refers to a coordination polyhedron around a specific atom. In YSZ, the coordination polyhedra around Y, Zr, and O atoms depend on the crystal structure. For YSZ, the central atoms could be zirconium (Zr), yttrium (Y), or oxygen (O). The zirconium and yttrium atoms may exhibit polyhedral coordination with the surrounding oxygen atoms. Oxygen atoms typically form polyhedra around cationic species, such as Zr and Y. Ellipsoids are often associated with the electron density distribution around an atom. In the context of electronic structure calculations, an ellipsoidal representation of the charge density or electron cloud is used to describe the spatial distribution of the electrons. In Fig.  2 (c), the zirconium and yttrium atoms in yttrium-stabilized zirconia (YSZ) have associated ellipsoids that describe their thermal vibrations. These ellipsoids were centered at the average positions of the Zr and Y atoms.

a coordination environment, b polyhedron, and c ellipsoid geometry of Yttrium-Stabilized Zirconia (YSZ)

Optical properties and dynamic stability of alumina and yttrium-stabilized zirconia

Band structure.

Figure  3 shows the band structure of alumina, which provides details of its electronic properties. The X-axis of the graph shows the high-symmetry points in the Brillouin zone. In this case, they are labeled as G, A, H, K, M, and L. These points correspond to definite crystallographic directions in the reciprocal lattice of the material. The broadening at the G and A points on the axis shown in Fig.  3 suggests the fanning out (dispersion) of the electronic states at the high-symmetry points. Therefore, this can indicate electronic transitions or electronic interactions occurring at G and A. The Y-axis gives the energy in electron volt (eV) units, which encompasses—− 20 to 20 eV, the energy level associated with the electronic states of the material.

Band structure of alumina

The band gap of alumina was 5.853 eV. The bandgap represents the energy difference between the top of the valence band and bottom of the conduction band. The band structure of alumina is crucial for understanding its electronic behavior. The wide bandgap of 5.853 eV [ 35 ] indicates that alumina is an insulating material, implying that it does not conduct electricity well. This property is desirable for applications such as esthetic crowns in dentistry. The insulating nature of alumina ensures that it does not interfere with electrical signals in the surrounding biological environment, making it suitable for use in dental crowns where electrical conductivity could be problematic. Overall, the band structure of alumina, with a significant band gap and specific broadening at high-symmetry points, supports its feasibility as a material for esthetic crowns, ensuring both electrical insulation and potentially favorable optical characteristics.

Moreover, the bandgap of alumina is a key factor in determining its stability. In general, materials with larger bandgaps are more stable. The bandgap represents the energy required to transition electrons from the valence to the conduction band. Alumina, which has a bandgap of 5.853 eV, is considered to have a relatively wide bandgap. A wide band gap indicates a large energy difference between the filled valence band and the empty conduction band. This large energy separation suggests that alumina is less prone to electron excitation and conductivity, making it an insulator. As an insulator, alumina is less likely to undergo spontaneous electron transitions, which contribute to its overall stability.

In contrast, in Fig.  4 , the Y-axis represents the energy values of the electronic bands in electron volts (eV). The range was—-15 to 15 eV. In any case, the x-axis is almost the same as the previous one. The bandgap of yttrium-stabilized zirconia was 7.631 eV [ 36 ]. The bandgap represents the energy difference between the valence and conduction bands. A larger bandgap indicates a better insulation. Moreover, materials with larger bandgaps are more stable. Yttrium-stabilized zirconia (YSZ) is known for its high strength and resistance to fracture, making it a popular choice for dental ceramics, especially for esthetic crowns.

Band structure graph of Yttrium-Stabilized Zirconia

Refractive index

The refractive index ( n ) of a material is a dimensionless quantity that provides a quantitative description of the bending or refraction of light as it enters a material from a different medium. The refractive index is often represented as (n + i k), where ( n ) is the real part and ( k ) is the imaginary part. The real part of the refractive index (n) describes how much the speed of light in the material is lowered with respect to the speed of light in vacuum. The positive values of ( n ) imply that the material is one in which the speed of light is attenuated. The imaginary part of the refractive index ( k ) is part of the optical index, which is directly related to the absorption or attenuation of light in the material.

The real part of the refractive index, the upward trend at 9 eV in Fig.  5 , suggests an increase in the refractive index, indicating increased slowing of light at this point. The downward trend at 24 Hz indicated a decrease in the refractive index, suggesting a reduction in the slowing of light. The imaginary part of the refractive index, the broadening from to the 6–24 frequency in the ( k ) values, indicates increased absorption or attenuation of light in this frequency range.

Refractive index of alumina

The refractive index is an important parameter in optical materials used for esthetic crown applications [ 37 ]. The positive values of ( n ) suggest that alumina can influence the speed of light, which is relevant for optical applications. The absorption indicated by ( k ) values may need to be considered, especially in esthetic applications where light transmission and appearance are crucial. Controlling the absorption properties is vital in esthetic crowns to prevent unwanted color distortions and to ensure that the crown appears natural.

The refractive index is directly associated with the dispersion of light. Achieving a harmonious color match with natural teeth requires careful control of the refractive index, particularly in the context of the broadening observed in the given frequency range. Figure  5 presents an overview of the optical behavior of alumina. The consistency of the refractive index and its response to light is critical for ensuring the optical clarity and esthetically pleasing appearance of an esthetic crown.

On the other hand, the refractive-index graph in Fig.  6 for yttrium-stabilized zirconia (YSZ) provides essential information about its optical properties, shedding light on its suitability for esthetic crown. The constant value of the refractive index ( n ) in the range of 6–18 frequency indicates that YSZ maintains a consistent optical behavior within this frequency range. This consistency is beneficial for achieving uniform optical properties in esthetic crowns. The sharp decrease from 5 to 1 on the y-axis suggests a substantial change in the refractive index, which may have implications for light transmission and color perception. The subsequent upward trend to 1.5 indicates a recovery in the refractive index. The sharp downward trend in the imaginary part of the refractive index ( k ) up to 10 eV indicates low light absorption within this frequency range. This is advantageous for esthetic crowns, as it suggests minimal color distortion due to absorption. The subsequent stabilization and slight upward trend of ( k ) beyond 10 eV indicate controlled absorption properties, contributing to the stability and color accuracy of the material. A constant refractive index within certain frequency ranges is desirable for achieving optical clarity and maintaining a natural appearance in esthetic crowns. The controlled absorption properties indicated by ( k ) contribute to the prevention of unwanted color distortions, ensuring that the crown closely matches natural teeth. The consistent refractive index values and controlled absorption properties suggest the stability of the optical performance of the YSZ. This is crucial for long-term durability and esthetic success of crown restorations.

Refractive index graph of Yttrium-Stabilized Zirconia

In comparison, alumina exhibits varying refractive index trends with absorption ( k ) in the observed frequency range. YSZ maintains a constant refractive index, indicating consistent optical behavior. YSZ exhibits better-controlled absorption, suggesting improved stability and color accuracy. The optical characteristics of YSZ make it a promising material for esthetic crown applications. In conclusion, yttrium-stabilized zirconia exhibits more desirable optical characteristics than alumina, making it a potentially superior material for esthetic crown applications, owing to its stable refractive index and controlled absorption properties.

• Mechanical properties

Stiffness matrix of alumina and yttrium-stabilized zirconia

The elastic stiffness constants (Cij) [ 38 ] of alumina, represented in GPa, provide crucial information regarding the response of the material to the applied stress and deformation, as shown in Table  1 . The data in Table  1 for the elastic stiffness constants of alumina are presented for a 6 × 6 matrix.

The high elastic stiffness constants, particularly those of the diagonal elements (C11, C22, C33, C44, C55, and C66), suggest that alumina is mechanically stable and can withstand stress and deformation. Stability is a crucial factor in dental restorations because it ensures that the crown material can endure forces exerted during mastication without undergoing significant deformation. The off-diagonal terms (C12, C13, C23, C14, C15, C16, C24, C25, and C26) indicate the anisotropic nature of alumina. Anisotropy implies that the mechanical properties of a material vary with the direction. Anisotropic behavior is important in esthetic crowns, as it allows for tailored mechanical properties depending on the orientation of the crown and its interaction with surrounding teeth.

The elastic stiffness constants allow the material to resist wear and deformation, thereby enhancing the long-term durability of the dental restorations. The values in the matrix that contribute to the mechanical integrity of the alumina crown would permit its use for esthetic crowns that need to withstand a variety of mechanical stresses, and knowing the elastic stiffness constants becomes important when we consider the proper design and fabrication of an esthetic crown because these values will need to be able to predict how the material under study will deform to the ideal loading conditions in such a way that its performance will be optimized; on the other hand, the elastic stiffness constants (Cij) of yttrium-stabilized zirconia (YSZ), also represented in GPa, are given in a 6 × 6 matrix in Table  2 .

The high values of the elastic stiffness constants, particularly those of the diagonal elements (C11, C22, C33, C44, C55, and C66), indicate that YSZ is mechanically stiff and exhibits excellent resistance to deformation under stress. High stiffness is advantageous in dental restorations because it contributes to the ability of the material to withstand forces exerted during biting and chewing. The diagonal terms of the matrix are identical, indicating isotropic behavior. Isotropy implies that the mechanical properties of the material are consistent in all the directions. Isotropic behavior simplifies the design and fabrication process for esthetic crowns, as the material responds uniformly to applied stress, ensuring predictable and reliable performance. The elastic stiffness constants influence the durability and resistance of the material to wear. YSZ’s stiffness of YSZ contributes to its ability to maintain its structural integrity over time, ensuring its long-term success as a dental restoration material. Understanding the elastic stiffness constants is crucial for designing esthetic crowns with precise mechanical properties. This enables dental professionals to tailor the material response to specific loading conditions and optimize the crown performance. While elastic stiffness is critical for mechanical performance, other factors, such as biocompatibility and esthetics, also play a role in the feasibility of YSZ for esthetic crowns. YSZ is known for its biocompatibility, and its natural color can contribute to visually appealing esthetic outcomes.

Comparing these values, YSZ has relatively higher values in its matrix than alumina, which means that YSZ is stiffer. In terms of the isotropic properties, the diagonal terms are the same for both values, indicating that they are isotropic. YSZ, which is stiffer, is more likely to have higher durability and resistance to deformation than alumina. Both materials offer precision in crown design owing to their isotropic behavior. The choice between them may depend on specific design requirements. This comparison indicates that YSZ generally has higher stiffness values, which may be advantageous in certain applications.

Average properties of alumina and yttrium-stabilized zirconia

The feasibility of alumina for esthetic crown applications is supported by its mechanical and optical properties derived from the average properties obtained through the Voigt, Reuss, and Hill averaging schemes listed in Table  3 .

The mechanical strength of a material is often characterized by parameters such as Young’s modulus ( E ), bulk modulus ( K ), and shear modulus ( G ). The Young’s modulus measures a material's stiffness, indicating how much it will deform under a given load. High values of the Young’s modulus imply that the material is stiff and resistant to deformation. A high Young’s modulus indicates that alumina can maintain its shape and resist bending or flexing, which is crucial for dental crowns subjected to biting and chewing forces. The bulk modulus is a measure of the resistance of a material to volume change under pressure. The high bulk modulus values indicate that the material was resistant to compression. In dental crowns that experience pressure from biting forces, a high bulk modulus is essential for maintaining the structural integrity of the crown and preventing undesirable changes in volume. The shear modulus measures the resistance of a material to deformation caused by shear stress. This represents the ability of a material to withstand the forces that act parallel to its surface. High shear modulus values imply that the material can resist shear forces, making it mechanically robust. In dental applications, resistance to shear force is crucial for the longevity and stability of crowns during mastication. The combination of the high Young's modulus and shear modulus values indicates that alumina can provide precise and stable crown fabrication. This is important for achieving an accurate fit and long-term durability of the dental crowns.

On the other hand, the average properties of yttrium-stabilized zirconia (YSZ) provide insights into its mechanical behavior in Table  4 , and these properties play a significant role in determining its feasibility as a material for esthetic crowns.

This averaging scheme provides an upper bound for the material properties. The high values of the bulk modulus (KV = 360.61 GPa), Young’s modulus (EV = 850.86 GPa), and shear modulus (GV = 384.39 GPa) indicate that YSZ is a stiff material with excellent resistance to deformation. This is advantageous for dental crowns because it suggests that YSZ can withstand forces associated with biting and chewing. This scheme provides a lower bound for the material properties. The values of the bulk modulus (KR = 360.61 GPa), Young's modulus (ER = 832.4 GPa), and shear modulus (GR = 373.18 GPa) obtained through Reuss averaging confirmed the stiffness and mechanical robustness of YSZ. The values of bulk modulus (KH = 360.61 GPa), Young’s modulus (EH = 841.66 GPa), and shear modulus (GH = 378.79 GPa) suggest that YSZ maintains a consistently high level of stiffness across the different averaging schemes.

The values of Poisson’s ratio obtained through different averaging schemes (νV = 0.10675, νR = 0.11528, νH = 0.111) suggest that YSZ has a relatively low Poisson’s ratio. A lower Poisson’s ratio is favorable for dental crowns because it indicates a lower susceptibility to deformation and better ability to maintain shape under stress. In general, the high stiffness, resistance to deformation, and low Poisson's ratio of YSZ, as indicated by its averaged properties, make it a feasible material for esthetic crowns.

In comparison, yttrium-stabilized zirconia (YSZ) exhibits a higher bulk modulus, Young's modulus, and shear modulus, along with a lower Poisson's ratio than alumina. These mechanical properties collectively suggest that YSZ is a stiffer and more resistant material, making it potentially more suitable for applications such as esthetic crowns, where mechanical strength and durability are crucial.

The eigenvalues of the stiffness matrix represent the natural frequencies at which a material vibrates when it is subjected to mechanical stimuli. In the context of alumina in Table  5 , the eigenvalues of its stiffness matrix (represented by λ1 to λ6) correspond to different modes of vibration and provide insights into its mechanical behavior.

The eigenvalues represent the stiffness or rigidity of alumina in different directions. Higher eigenvalues suggest higher stiffness in these specific directions, contributing to the overall stability of the material. The eigenvalues are associated with the natural frequencies of vibrations. Understanding these frequencies is crucial in applications where the material may be subjected to mechanical vibrations, ensuring that the material does not resonate or deform undesirably under specific loads.

In the context of esthetic crowns, the eigenvalues provide insights into how alumina responds to forces and stresses. Higher eigenvalues indicate a greater resistance to deformation, which is essential for maintaining the structural integrity of dental restorations.

On the other hand, in the context of yttrium-stabilized zirconia (YSZ), the eigenvalues (λ1 to λ6) provide insights into its mechanical behavior and structural characteristics in Table  6 . Equal values of the first three eigenvalues (λ1, λ2, and λ3) indicate isotropic or uniform stiffness in those directions. This property is beneficial in dental applications where consistent material behavior is desired. The last three eigenvalues (λ4, λ5, and λ6) were higher, indicating an increased stiffness in specific directions. This anisotropic stiffness provides YSZ with tailored mechanical properties, making it suitable for applications in which strength and resistance to deformation are crucial.

Moreover, the higher eigenvalues in certain directions suggest that YSZ can effectively resist deformations and stresses. This durability is essential for esthetic crowns to ensure long-term performance without mechanical failure.

In contrast, the eigenvalues of the stiffness matrix highlight the mechanical differences between yttrium-stabilized zirconia and alumina. YSZ exhibits a more isotropic stiffness profile with a higher overall stiffness, making it suitable for applications that require enhanced mechanical properties, such as esthetic crowns in dentistry.

Elastic moduli of yttrium-stabilized zirconia and alumina

Table 7 provides information about the variations in the elastic moduli of alumina, including the Young's modulus (Fig.  7 and (Additional file 1 : Figure S1 for 2D representation), linear compressibility, shear modulus, and Poisson's ratio. These variations are crucial for understanding the response of the material to mechanical stress and play a significant role in the suitability of alumina for esthetic crown applications.

3D representation of Young’s modulus of alumina

The range from E min (266.16 GPa) to E max (399.71 GPa) represents the variation in Young's modulus. This variation describes the stiffness of the material and its ability to withstand deformation under an applied stress. An anisotropy value of 1.502 indicates that the stiffness of the material varied in different crystallographic directions.

The variation from β min (1.5841 TPa^–1) to β max (1.8125 TPa^–1) represents the linear compressibility of alumina (Fig.  8 and (Additional file 1 : Figure S2 for 2D representation). This property indicates that the material responds to compressive stress. Furthermore, the range from G min (103.75 GPa) to G max (173.38 GPa) represents the variation in the shear modulus (Fig.  9 and (Additional file 1 : Figure S3 for 2D representation). The shear modulus reflects the resistance of a material to deformation under shear stress. Moreover, the range from ν min (0.053073) to ν max (0.3787) represents the variation in Poisson’s ratio (Fig.  10 and (Additional file 1 : Figure S4 for 2D representation). This ratio describes the tendency of the material to contract laterally when longitudinally compressed.

3D representation of linear alumina compressibility

3D representation of the shear modulus of alumina

3D representation of Poisson’s ratio of alumina

The anisotropy values for each property indicate the extent to which the material varies in different crystallographic directions, and the axis values indicate the orientation of the crystallographic axes with respect to the measurement axes. Nonetheless, this anisotropy allows the stiffness of alumina to be tailored in different directions. For dental applications, crown materials must closely resemble the mechanical properties of the natural teeth. The anisotropy values and axis information are helpful during fabrication to orient the crown with respect to the optimized mechanical properties of the material for the given directions.

In conclusion, variations in the elastic moduli of alumina are vital for tailoring the mechanical properties of materials to meet the specific requirements of esthetic crown applications. These properties ensure that the crown exhibits appropriate stiffness, deformation response, and dimensional stability, thereby contributing to the overall success of the dental restorations.

However, variations in the elastic moduli of yttrium-stabilized zirconia (YSZ) are important for esthetic crown applications for several reasons.

Young’s modulus ( E ) represents the stiffness of the material (Table  8 , Fig.  11 , and (Figure S5 for the 2D representation). The variation from E min (716.92 GPa) to E max (932.53 GPa) allows for controlled stiffness in different directions. This is crucial for mimicking the mechanical behavior of natural teeth and ensuring that the esthetic crown exhibits an appropriate level of rigidity.

3D representation of Young’s modulus of Yttrium-Stabilized Zirconia

The constant values of linear compressibility ( β min and β max at 0.92437 TPa^–1) indicate a consistent response to compressive stress (Fig.  12 and (Additional file 1 : Figure S6 for 2D representation). In esthetic crown applications, where the material may experience compressive forces during biting and chewing, predictable linear compressibility is essential for stability and reliability.

3D representation of the linear compressibility of Yttrium-Stabilized Zirconia

The variation in shear modulus ( G min to G max from 306.73 GPa to 436.17 GPa) reflects YSZ's ability to resist deformation under shear stress (Fig.  13 and (Additional file 1 : Figure S7 for 2D representation). This property is critical for ensuring that the esthetic crown maintains its structural integrity, especially in areas where shear forces are applied during mastication.

3D representation of the shear modulus of Yttrium-Stabilized Zirconia

The range of Poisson's ratio values ( ν min to ν max from − 0.0057521 to 0.20403) provides insights into the response of YSZ to longitudinal compression (Fig.  14 and (Additional file 1 : Figure S8 for 2D representation). Understanding the lateral contraction behavior is vital for preventing dimensional changes and maintaining the stability of the esthetic crown. The anisotropy values and axis information help align the crown orientation with the optimal mechanical properties of YSZ in specific directions. This enables manufacturers to customize crown structures based on the anisotropic nature of materials.

3D representation of Poisson’s ratio of Yttrium-Stabilized Zirconia

Overall, these variations in elastic moduli allow tailoring of the mechanical properties of YSZ to meet the specific demands of esthetic crown applications. This material can be designed to provide the right balance between stiffness, compressibility, shear resistance, and dimensional stability, thereby ensuring the long-term success and functionality of dental restorations. YSZ generally exhibits higher Young’s modulus, shear modulus, and anisotropy values than alumina.

Overall, YSZ and alumina possess different strengths and advantages when used in esthetic crowns, and the former seems to be a promising material with high mechanical strength, stable optical properties, and geometries. Alumina, on the other hand, shows a unique CKP geometry, as well as stable band structures and esthetically desirable optical properties, making it suitable for use in esthetic crown designs. In conclusion, dental practitioners should have knowledge of the scientific basis for material selection; however, the best choice may ultimately be specific to individual cases, requiring a delicate balancing act.

Availability of data and materials

The data are available upon genuine request.

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Acknowledgements

1. All the authors are thankfull to the King Khalid University , Saudi Arabia for the financial Support. 2. All the authors would like to thank Mr Wahid Hussain for his assistance with Quantum Analysis.

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through the Small Group Research Project under Grant Number RGP1/331/44.

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Department of Dental Technology, COAMS, King Khalid University, Abha, Saudi Arabia

Ravinder S. Saini, Abdulkhaliq Ali F. Alshadidi, Vishwanath Gurumurthy, Abdulmajeed Okshah, Sunil Kumar Vaddamanu & Rayan Ibrahim H. Binduhayyim

Department of Prosthetic Dentistry, College of Dentistry, King Khalid University, Abha, Saudi Arabia

Saurabh Chaturvedi

Department of Restorative Dental Sciences, College of Dentistry, King Khalid University, Abha, Saudi Arabia

Shashit Shetty Bavabeedu

Department of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, 600 077, India

Artak Heboyan

Department of Prosthodontics, Faculty of Stomatology, Yerevan State Medical University after Mkhitar Heratsi, Str. Koryun 2, 0025, Yerevan, Armenia

Department of Prosthodontics, School of Dentistry, Tehran University of Medical Sciences, North Karegar St, Tehran, Iran

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Conceptualization and methodology: Ravinder Saini, Abdulkhaliq Shadidi. Data curation and formal analysis: Vishwanath Gurumurthy, Sunil Vaddamanu. Investigation and resources: Abdulmajeed Okshah, Rayan Binduhayyim. Original draft preparation: Ravinder S Saini, and Artak Heboyan. Writing, reviewing and editing: Saurabh Chaturvedi, Shashit Bavabeedu. Supervision and project administration: Ravinder Saini, Artak Heboyan. Funding acquisition: Ravinder Saini.

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Additional file1 : fig. s1..

2D representation of Young's modulus of Alumina in xy, xz and yz plane. Fig. S2. 2D representation of linear compressibility of Alumina in xy, xz and yz plane. Fig. S3. 2D representation of Shear modulus of Alumina in xy, xz and yz plane. Fig. S4. 2D representation of Poisson's ratio of Alumina in xy, xz and yz plane. Fig. S5. 2D representation of Youngs’s modulus of Yttrium-Stabilized Zirconia in xy, xz and yz plane. Fig. S6. 2D representation of linear compressibility of Yttrium-Stabilized Zirconia in xy, xz and yz plane. Fig. S7. 2D representation of Shear modulus of Yttrium-Stabilized Zirconia in xy, xz and yz plane. Fig. S8. 2D representation of Poisson’s ratio of Yttrium-Stabilized Zirconia in xy, xz and yz plane.

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Saini, R.S., Alshadidi, A.A.F., Gurumurthy, V. et al. Quantum mechanical analysis of yttrium-stabilized zirconia and alumina: implications for mechanical performance of esthetic crowns. Eur J Med Res 29 , 254 (2024). https://doi.org/10.1186/s40001-024-01851-2

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How Medical–Dental EHR Integration Can Improve Diabetes Care

• a An associate research scientist at the Marshfield Clinic Research Institute's Center for Oral and Systemic Health in Wisconsin. A trained dental surgeon with expertise in healthcare management and biomedical and health informatics, she has been advancing medical-dental integration for over 8 years. Her interests include medical-dental integration, dental informatics, and investigating oral and systemic health integration.
• b Director of the Office of Research Support Services and a senior research scientist at the Marshfield Clinic Research Institute in Wisconsin. She is a frequent speaker and consultant for institutional review boards and research ethics boards and is professionally active in the organization Public Responsibility in medicine and research. Her scholarship focuses on research ethics (especially the internet), compliance, and regulations.
• c Chief research officer and system vice president of research at Advocate Aurora Health. He previously served as the executive director of Marshfield Clinic Research Institute and the chief dental informatics officer of Family Health Center of Marshfield. He was the founding director of the Institute for Oral and Systemic Health at the Marshfield Clinic Health System (MCHS) and helped integrate MCHS' electronic dental record with its propriety electronic medical record. His research interests lie in the design and architecture of electronic health records and data warehouses, artificial intelligence, machine learning, clinical decision supports and expert systems, information modeling, and the oral-systemic disease relationship.

Since the mid-1990s, poor oral health has been neglected as a public health threat, despite its recognition as epidemic in scale by the US Department of Health and Human Services Office of the Surgeon General. Americans' poor oral health influences their overall health and, from a population standpoint, incurs dire economic and human costs. This article describes how health information transfer within the Marshfield Clinic Health System's integrated medical and dental practice can improve diabetes care. This article also considers ethics and equity implications of improving MDP electronic health record interoperability in this large, rural Wisconsin organization.

Quiz Ref ID Chronic diseases are best managed by coordinating care, such that interventions are discussed within teams of cross-disciplinary clinicians and efficiently incorporated into service delivery, facilitating patients' understanding of their options, care, and recovery plans. However, separate medical-dental service delivery streams and reimbursement systems and limited interprofessional training continue to undermine effective chronic disease management strategies. 1 -  3 Quiz Ref ID Oral health's importance to general health is not routinely discussed in primary care settings, which results in missed research and patient education opportunities, prevention implementation, and dental referrals. 4 Moreover, siloed medical and dental practice inequitably affects patients with chronic conditions, since separate storage and inadequate sharing of patients' health records can compromise quality of care, especially during routine medical or dental appointments or emergency department visits .

This article describes the need for improved electronic health record (EHR) interoperability and how health information transfer within the Marshfield Clinic Health System's (MCHS) integrated medical and dental practice can improve diabetes care.

In 2000, the US Surgeon General recognized oral disease as a “silent epidemic” 5 that increases morbidity and the economic burden of disease. 6 -  8 Chronic oral diseases (eg, dental caries and periodontal disease) are linked to patients' development of chronic diseases, such as diabetes. 9 , 10 Total deaths in the United States due to diabetes is expected to increase by 38% between 2015 and 2030, 11 and several dental diseases are common complications of diabetes. 12 Periodontal disease interventions could lower annual costs for patients with Type 2 diabetes, stroke, heart disease, and pregnancy and reduce inpatient admissions. 13 Quiz Ref ID Despite these benefits, geriatric, pediatric, low-income, uninsured, underinsured, and chronically ill patients are particularly likely to have their oral health needs underestimated or missed altogether by clinicians. 14 One reason for these patients' unmet oral health needs is that operable health information exchange (HIE) between medical and dental clinicians about such patients is nonexistent or inferior. 15 The rest of this article is devoted to explaining why HIE is a patient-centered care practice that can improve patients' outcomes and reduce healthcare utilization for patients with diabetes. 16 -  18

The Institute for Electrical and Electronics Engineering defines interoperability as “the ability of two or more systems or components to exchange information and to use the information that has been exchanged.” 19 To help promote interoperability and holistic patient care, the Institute of Medicine developed a list of health information technology (HIT) recommendations to support effective, efficient flow of patients' health information across care settings . 20 Quiz Ref ID EHR integration promotes health equity by improving HIT interoperability and enabling secure HIE. 21 Quiz Ref ID Physical and virtual integration of organizations' EHR infrastructure can generally be implemented in 1 of 3 ways:

Ad hoc implementation : fully integrated medical-dental EHR architecture conforms to national standards.

Broad implementation : EHR architecture supports interoperability within proprietary clinical information systems (eg, EPIC's Wisdom module 22 ).

Universal implementation : EHR architecture supports interoperability across different clinical information systems (eg, regional HIE platforms).

Some organizations, such as the MCHS, Kaiser Permanente, HealthPartners, and federally qualified health centers (FQHCs), have adopted and implemented virtual and physical EHR integration where dental and medical practices are geographically close in order to promote care continuity for patients, including those with diabetes. 4

The MCHS was founded in 1916 as Marshfield Clinic and is one of the largest comprehensive integrated health systems in the United States. 23 , 24 A multispecialty group and health professions learning environment, MCHS serves communities in health professional shortage areas 25 and patients living in central, western, and northern regions of Wisconsin and in Michigan's Upper Peninsula. 26 In the early 2000s, oral health services access for rural Medicare- and Medicaid-eligible patients was substantially limited. Family Health Center (FHC), an FQHC that has served low-income, underinsured, and uninsured patients since 1974, partnered with MCHS in 2002 to establish a dental safety net. Tendental care sites now serve more than 50000 unique patients annually, 24 regardless of income or insurance status, with a goal of mitigating health inequity (see 1 ).

a Data from Marshfield Clinic Health System enterprise data warehouse.

Although MCHS services are supported by a customized medical-dental integrated EHR (iEHR), 23 , 24 barriers to medical-dental care integration for patients with diabetes remained and were further studied. Based on 2018–2019 aggregate EHR data, 8% of MCHS medical and dental patients (6363 of 71 811) were diagnosed with diabetes. Approximately, 7% of patients diagnosed with diabetes (32 131 of 472 591) only visited MCHS medical, but not dental, centers. With these data in mind, investigators first sought to understand perceived practice gaps and barriers to medical-dental integration. 27 , 28 Surveys 2 , 3 , 29 and focus groups were conducted with MCHS/FHC medical and dental clinicians to assess their knowledge of, attitudes toward, and practice of integrated care. 2 , 3 , 29 , 30 Participants' responses informed development of educational tools for clinicians and patients, informed which metrics would be used to assess these tools' efficacy, and led to incorporating a clinical decision support alert tool (CDSAT) into practice to help diagnose patients with diabetes.

Eligibility criteria in the CDSAT of a patient's iEHR alert primary care clinicians to conduct visual oral examinations of patients with diabetes. Four years after the CDSAT was implemented in 2014 at 2 pilot MCHS medical sites, evaluation of the CDSAT revealed that it triggered a total of 7723 patients' EHRs to issue an alert. In response to alerts, primary care clinicians visually orally examined 4943 patients with diabetes (64%); among those patients, 170 were referred to dentists and 626 were advised to visit a dentist. Additionally, another study developed risk assessment tools using informatics techniques . As informatics tools help predict diabetes risk among patients in dental settings, 31 the study team screened patients for risk of diabetes using machine-learning techniques that extracted medical-dental variables from the iEHR. Using this predictive tool in MCHS dental centers enabled patients at risk for developing diabetes to be identified by dentists, who then referred them to physicians.

Centers for Medicaidand Medicareservices data indicate that MCHS was the most successful participant in a 2011 Physician Group Practice demonstration project, as it earned 57% of the $107.6 million in gross savings realized over a 5-year period, most of which was distributed among the 10 participating MCHS institutions based on performance. 32 Outcomes of the demonstration project validated that integrating medical and dental care, supported by use of informatics and quality tracking, delivered high-quality care at reduced cost. 32 Subsequent research and quality improvement projects have also demonstrated improvements in healthcare outcomes, patient safety, and patient satisfaction. 26 -  28 , 33 While medical-dental HIE is the focus of this case study, integrated care, according to the World Health Organization, is about patients' experiences—specifically, about “the organization and management of health services so that people get the care they need, when they need it, in ways that are user-friendly, achieve the desired results and provide value for money.” 34 Integrated service delivery facilitated by HIE also nourishes professional ethics and health equity. 35 -  37

MCHS/FHC established medical-dental interoperability, via EHR integration, to identify diabetic and prediabetic patients in predominantly rural areas of Wisconsin and Michigan and to facilitate such patients' access to prevention and management interventions. Integrated care holds promise for alleviating health inequities, specifically for rural populations. President Joseph Biden has been pressured to appoint a rural health “czar,” 38 and medical-dental integration successes of the MCHS/FHC model could be replicated in other US regions and for other chronic disease states.

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Neel Shimpi, BDS, MM, PhD is an associate research scientist at the Marshfield Clinic Research Institute's Center for Oral and Systemic Health in Wisconsin. A trained dental surgeon with expertise in healthcare management and biomedical and health informatics, she has been advancing medical-dental integration for over 8 years. Her interests include medical-dental integration, dental informatics, and investigating oral and systemic health integration; Elizabeth Buchanan, PhD is the director of the Office of Research Support Services and a senior research scientist at the Marshfield Clinic Research Institute in Wisconsin. She is a frequent speaker and consultant for institutional review boards and research ethics boards and is professionally active in the organization Public Responsibility in medicine and research. Her scholarship focuses on research ethics (especially the internet), compliance, and regulations; Amit Acharya, BDS, MS, PhD is the chief research officer and system vice president of research at Advocate Aurora Health. He previously served as the executive director of Marshfield Clinic Research Institute and the chief dental informatics officer of Family Health Center of Marshfield. He was the founding director of the Institute for Oral and Systemic Health at the Marshfield Clinic Health System (MCHS) and helped integrate MCHS' electronic dental record with its propriety electronic medical record. His research interests lie in the design and architecture of electronic health records and data warehouses, artificial intelligence, machine learning, clinical decision supports and expert systems, information modeling, and the oral-systemic disease relationship.

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Toroidally focused ultrasonic flaw detectors

• Acoustic Methods
• Published: 28 July 2011
• Volume 47 , pages 308–310, ( 2011 )

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• A. V. Shevelev 1 &
• Zh. V. Zatsepilova 2  

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New-type toroidally focused ultrasonic flaw detectors, whose application provides an appreciable increase in the flaw detection rate with retention of high sensitivity to flaws, are considered. The construction of a flaw detector is presented, the sizes of a gauge for the formation of the toroidal surface of a lens are given, and the technology of the manufacturing of a toroidal lens is described.

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Remote diagnostics of soft solids using nonlinear acoustic methods

Ultrasonic flaw detection: adjustment and calibration of equipment using samples with cylindrical drilling.

Influence of Pitch of Ultrasonic Antenna Array on Efficiency of Extraction of a Signal from Structural Noise in Flaw Detection

Ermolov, I.N., Aleshin, N.P., and Potapov, A.I., Nerazrushayushchii control’ (Nondestructive Testing), book 2: Akusticheskie metody kontrolya (Acoustic Testing), Moscow: Vysshaya shkola, 1991.

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Nerazrushayushchii kontrol’ (Spravochnik) (Nondestructive Testing: Handbook), Klyuev, V.V., Ed., vol. 3: Ul’trazvukovoi kontrol’ (Ultrasonic Testing), Moscow: Mashinostroenie, 2006.

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Elektrostal Polytechnic Institute, Branch of the National University of Science and Technology “MISIS”, ul. Pervomaiskaya 7, Elektrostal, Moscow oblast, 144000, Russia

A. V. Shevelev

Elektrostal Heavy Engineering Plant JSC, ul. Krasnaya 19, Elektrostal, Moscow oblast, 144005, Russia

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Original Russian Text © A.V. Shevelev, Zh.V. Zatsepilova, 2011, published in Defektoskopiya, 2011, Vol. 47, No. 5, pp. 19–22.

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Shevelev, A.V., Zatsepilova, Z.V. Toroidally focused ultrasonic flaw detectors. Russ J Nondestruct Test 47 , 308–310 (2011). https://doi.org/10.1134/S1061830911050093

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Received : 14 January 2011

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DOI : https://doi.org/10.1134/S1061830911050093

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Contents of JIDMR-2021-Vol.14–No.1

Journal of International Dental and Medical Research

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Fatal Traffic Risks With a Total Solar Eclipse in the US

• 1 Department of Medicine, University of Toronto, Toronto, Ontario, Canada
• 2 Evaluative Clinical Science Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada
• 3 Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
• 4 Division of General Internal Medicine, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
• 5 Center for Leading Injury Prevention Practice Education & Research, Toronto, Ontario, Canada
• 6 Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
• 7 Centre for Clinical Epidemiology & Evaluation, University of British Columbia, Vancouver, British Columbia, Canada

A total solar eclipse occurs when the moon temporarily obscures the sun and casts a dark shadow across the earth. This astronomical spectacle has been described for more than 3 millennia and can be predicted with high precision. Eclipse-related solar retinopathy (vision loss from staring at the sun) is an established medical complication; however, other medical outcomes have received little attention. 1

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Redelmeier DA , Staples JA. Fatal Traffic Risks With a Total Solar Eclipse in the US. JAMA Intern Med. Published online March 25, 2024. doi:10.1001/jamainternmed.2023.5234

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Published on 25.4.2024 in Vol 26 (2024)

Leveraging Large Language Models for Improved Patient Access and Self-Management: Assessor-Blinded Comparison Between Expert- and AI-Generated Content

Authors of this article:

Original Paper

• Xiaolei Lv 1, 2, 3, 4, 5, 6 , MSc   ; 
• Xiaomeng Zhang 1, 2, 3, 4, 5, 6 , PhD   ; 
• Yuan Li 1, 2, 3, 4, 5, 6 , MSc   ; 
• Xinxin Ding 1, 2, 3, 4, 5, 6 , PhD   ; 
• Hongchang Lai 1, 2, 3, 4, 5, 6 , Prof Dr Med, PhD   ; 
• Junyu Shi 1, 2, 3, 4, 5, 6 , PhD  

1 Department of Oral and Maxillofacial Implantology, Shanghai PerioImplant Innovation Center, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

2 College of Stomatology, Shanghai Jiao Tong University, Shanghai, China

3 National Center for Stomatology, Shanghai, China

4 National Clinical Research Center for Oral Diseases, Shanghai, China

5 Shanghai Key Laboratory of Stomatology, Shanghai, China

6 Shanghai Research Institute of Stomatology, Shanghai, China

Corresponding Author:

Junyu Shi, PhD

Department of Oral and Maxillofacial Implantology, Shanghai PerioImplant Innovation Center

Shanghai Ninth People's Hospital

Shanghai Jiao Tong University School of Medicine

Quxi Road No 500

Shanghai, 200011

Phone: 86 21 23271699 ext 5298

Email: [email protected]

Background: While large language models (LLMs) such as ChatGPT and Google Bard have shown significant promise in various fields, their broader impact on enhancing patient health care access and quality, particularly in specialized domains such as oral health, requires comprehensive evaluation.

Objective: This study aims to assess the effectiveness of Google Bard, ChatGPT-3.5, and ChatGPT-4 in offering recommendations for common oral health issues, benchmarked against responses from human dental experts.

Methods: This comparative analysis used 40 questions derived from patient surveys on prevalent oral diseases, which were executed in a simulated clinical environment. Responses, obtained from both human experts and LLMs, were subject to a blinded evaluation process by experienced dentists and lay users, focusing on readability, appropriateness, harmlessness, comprehensiveness, intent capture, and helpfulness. Additionally, the stability of artificial intelligence responses was also assessed by submitting each question 3 times under consistent conditions.

Results: Google Bard excelled in readability but lagged in appropriateness when compared to human experts (mean 8.51, SD 0.37 vs mean 9.60, SD 0.33; P =.03). ChatGPT-3.5 and ChatGPT-4, however, performed comparably with human experts in terms of appropriateness (mean 8.96, SD 0.35 and mean 9.34, SD 0.47, respectively), with ChatGPT-4 demonstrating the highest stability and reliability. Furthermore, all 3 LLMs received superior harmlessness scores comparable to human experts, with lay users finding minimal differences in helpfulness and intent capture between the artificial intelligence models and human responses.

Conclusions: LLMs, particularly ChatGPT-4, show potential in oral health care, providing patient-centric information for enhancing patient education and clinical care. The observed performance variations underscore the need for ongoing refinement and ethical considerations in health care settings. Future research focuses on developing strategies for the safe integration of LLMs in health care settings.

Introduction

Since the launch of ChatGPT by OpenAI [ 1 ] in November 2022, the model has attracted significant global attention, securing over a million users within just 5 days of its release [ 2 ]. ChatGPT is a notable representative of large language models (LLMs), built upon the solid foundation of the GPT architecture [ 3 ]. In today’s technology landscape, other technology giants, including Google and Microsoft, have also developed proprietary and open-source LLMs. These models, pretrained on extensive unlabeled text data sets using self-supervised or semisupervised learning techniques, demonstrate exceptional natural language processing capabilities [ 4 ]. Their advanced capabilities in understanding and generating human-like responses make them particularly relevant for applications in health care, a sector that increasingly relies on digital information and interaction.

The significant potential of such models in the health care sector has captured wide attention among medical professionals [ 5 ]. Notably, without any specialized training or reinforcement, ChatGPT-3.5 performed at or near the passing threshold for the United States Medical Licensing Examination [ 6 ]. This underscores its vast capabilities within medicine, such as retrieving knowledge, aiding clinical decisions, summarizing key findings, triaging patients, and addressing primary care issues. Given its proficiency in generating human-like texts, one of the key applications of LLMs lies in improving health care access and quality through better patient information dissemination.

Early studies have primarily assessed its performance in responding to fundamental questions concerning cardiovascular diseases, cancers, and myopia, yielding encouraging results [ 7 - 10 ]. However, the broader impact of LLMs on patient health care access and quality, particularly in specialized areas such as oral health, has yet to be fully explored. Oral diseases affect over 3.5 billion people worldwide, leading to significant health and economic implications and substantially reducing the quality of life for those affected [ 11 ]. The historical marginalization of oral health care has resulted in considerable gaps in patient literacy, hygiene awareness, and medical consultations [ 12 - 14 ], highlighting a critical area where LLMs could make a significant difference. LLMs have the potential to bridge these gaps by providing accessible, accurate information and advice, thus enhancing patient understanding and self-management. Furthermore, the scarcity of health workers and disparities in resource distribution exacerbate these issues [ 15 , 16 ]. In this context, LLMs, with their rapid advancements, offer a promising avenue for enhancing health care access and quality across various domains [ 17 , 18 ]. A US survey revealed that about two-thirds of adults seek health information on the web and one-third attempt self-diagnosis via search engines [ 19 ]. This trend underscores the growing role of LLMs in digital health interventions [ 20 ], potentially enabling patients to overcome geographical and linguistic barriers in accessing high-quality medical information.

To explore this potential, this study focuses on oral health as an example, assessing the ability of the leading publicly available LLMs, such as Google Bard (Alphabet Inc; subsequently rebranded as Gemini) [ 21 ], ChatGPT-3.5, and ChatGPT-4, in providing patient recommendations for the prevention, screening, and preliminary management of common oral health issues compared to human experts. Both experienced dentists and lay users without medical backgrounds have been invited to evaluate the responses blindly along specified criteria. Our findings are intended to offer valuable insights into the potential benefits and risks associated with using LLMs for addressing common medical questions.

Ethical Considerations

Participants in this study were sourced from our earlier research project, “Bio-bank Construction of Terminal Dentition,” which was approved by the Ethical Committee of Shanghai Ninth People’s Hospital, China (SH9H-2021-T394-2). All participants provided written informed consent prior to the commencement of the study, which clarified their rights to participate and the ability to withdraw from the study at any time. All personal information in this study was anonymized to ensure the privacy and confidentiality of participant data. No compensation was provided to the participants.

Study Design

Figure 1 illustrates the overall study flow diagram. From August 9 to 23, 2023, a questionnaire survey was conducted among outpatients in the Department of Oral and Maxillofacial Implantology at Shanghai Ninth People’s Hospital to inquire about their primary concerns regarding periodontal and implant-related diseases. Informed by the latest consensus reports on periodontal and peri-implant diseases [ 22 ] and clinical experience in tertiary care for periodontology and implantology, our specialist panel (YL, Ke Deng, and Miaoxuan Dai) listed a set of initial questions. Patients rated these on a scale from 0=no concern to 10=extremely concerned and could add any other significant concerns. The questionnaire was administered in Chinese, and the translation and cultural adaptation to English adhered to established guidelines for cross-cultural questionnaire adaptation [ 23 ]. The back translation method was used to ensure both accuracy and cultural appropriateness. After collecting the surveys, the expert panel conducted a thorough review and consolidation process. This involved analyzing patient ratings and comments to identify the most pertinent questions. As a result, a refined set of 40 questions was developed ( Multimedia Appendix 1 ). These questions comprehensively covered 6 domains of periodontal and dental implant-related diseases, including patient education, self-prevention, diagnosis, treatment, management, and support.

From September 4 to 18, 2023, the panel was asked to generate human expert responses to these questions. At the same time, each question was also input into the ChatGPT-3.5, ChatGPT-4, and Google Bard interface, and the subsequent 3 sets of responses were recorded. For the interactions with the LLMs, all responses were generated based on default parameter settings, including temperature and maximum tokens, without any additional specific parameter adjustments. Each question corresponds to a new session and finally has 4 responses. The 4 sets of responses were anonymized and randomly shuffled for evaluation by 5 experienced dentists (JS, Xinyu Wu, Xiaoyu Yu, XZ, and XD) and 5 lay users, respectively, along the axes presented in Multimedia Appendix 2 . The assignment was concealed from the evaluators and outcome examiners (XL and Xue Jiang).

To further understand the stability of responses, each question was submitted to the artificial intelligence (AI) interfaces 3 times from October 28 to 30, 2023. This process was conducted at the same time each day over a 3-day span with constant environmental conditions and model parameters. Each set of 3 responses was independently evaluated by 2 experienced dentists (JS and XL).

Readability Evaluation

The readability of each response was assessed using Readable (Added Bytes), a web-based readability tool featuring multiple indices. Three of the most widely used parameters were recorded. (1) Flesch-Kincaid Grade Level: this is equivalent to the US grade level of education. It shows the required education to be able to understand a text; (2) Gunning Fog Index: this generates a grade level between 0 and 20. It estimates the education level required to understand the text. Text aimed at the public should aim for a grade level of around 8. Text above a 17 requires a graduate-level understanding; and (3) Flesch Reading Ease Score: this is between 1 and 100, with 100 being the highest readability score. Scoring between 70 and 80 is equivalent to school grade level 8. This means text should be fairly easy for the average adult to read.

Dentist Evaluation

Five experienced dentists (JS, Xinyu Wu, Xiaoyu Yu, XZ, and XD) were asked to grade each response using the visual analog scale (VAS) along the following 3 axes ( Multimedia Appendix 2 ). The final results were synthesized based on the scores from all 5 dentists. The average was calculated after removing extreme values to minimize errors.

• Appropriateness: How appropriate is this answer to the patient asking? (0=totally inappropriate or incorrect and 10=totally appropriate)
• Comprehensiveness: Does the answer omit any content it should not? (0=yes, great clinical significance and 10=no missing content)
• Harmlessness: What is the extent and likelihood of possible harm? (0=severe harm and 10=no harm)

Lay User Evaluation

Five lay users were also asked to grade each response using the VAS along the following 2 axes ( Multimedia Appendix 2 ). Final results were synthesized based on the scores from all 5 lay users, and the average was calculated after removing extreme values.

• Intent capture: How well does the answer address the intent of the question? (0=does not address query and 10=addresses query)
• Helpfulness: How helpful is this answer to the user? (0=not helpful at all and 10=very helpful)

Further Evaluation of LLMs in Different Conditions and Domains

To further investigate whether the responses of LLMs differ across various conditions and domains, detailed subanalyses were conducted on 2 oral issues (periodontitis and dental implant) and 6 medical care domains (patients’ education, prevention, diagnosis, treatment, management, and support).

Stability Evaluation

Each question was submitted to the AI interfaces 3 times, and the responses were recorded. Two experienced dentists (JS and XL) independently evaluated each set of 3 responses. Responses were graded as “correct” or “incorrect” based on clinical judgment and the content or as “unreliable” if the 3 responses were inconsistent. Any set with at least 1 incorrect response was graded as incorrect.

Statistical Analysis

Statistical analyses were conducted using SAS software (version 9.4; SAS Institute) and GraphPad Prism 9 (GraphPad Software, Inc). Quantitative data of normal distribution were summarized as means and SDs. Intraclass correlation coefficient (ICC) was used to access interrater agreement. Repeated measures ANOVA was used to compare scores across the LLMs and human experts. Additionally, paired chi-square tests were used to assess the stability of AI responses. Statistical significance was set at a P <.05.

Readability Evaluation Results

In the readability evaluation, detailed in Table 1 and Figure 2 , Google Bard was found to be the most readable for the public. It scored the lowest on Flesch-Kincaid Grade Levels (mean 7.86, SD 0.96) and Gunning Fog Index (mean 9.62, SD 1.11) and the highest on the Flesch Reading Ease Score (mean 61.72, SD 6.64), indicating it was easier to comprehend and had superior readability (all P <.001). Furthermore, the word count for all 3 LLMs, averaging over 300 words, was significantly higher than the approximately 100 words typical for human experts.

a Flesch-Kincaid Grade and Gunning Fog Index show the education level needed for understanding; a lower score means that it is easier.

b Flesch Reading Ease Scores from 1 to 100, with a higher score meaning easier to read.

Dentist Evaluation Results

Table 2 and Figure 3 present the evaluation results of dentists. Google Bard demonstrated significantly lower appropriateness score than human experts (mean 8.51, SD 0.37 vs mean 9.60, SD 0.33; P =.03), while ChatGPT-3.5 and ChatGPT-4 got comparable scores (mean 8.96, SD 0.35 and mean 9.34, SD 0.47, respectively). Google Bard also showed a great level of missing content than ChatGPT-3.5 (mean 8.40, SD 0.60 vs mean 9.46, SD 0.14; P =.04). No other difference of comprehensiveness was significant between groups. All 3 LLMs showed superior harmlessness scores comparable with human experts (Google Bard: mean 9.34, SD 0.11; ChatGPT-3.5: mean 9.65, SD 0.20; ChatGPT-4: mean 9.69, SD 0.41; and human experts: mean 9.68, SD 0.4, out of a maximum score of 10). The ICC indicated “substantial” agreement among dentists with a value of 0.715.

Lay User Evaluation Results

Table 2 and Figure 4 display the evaluation results of lay users. No significant difference between the responses of LLMs and human experts, with both effectively capturing user intent and providing helpful answers for them (all P >.05). The ICC indicated “moderate” agreement among lay users with a value of 0.586.

Subanalysis Results

Subanalyses were conducted across the 2 oral issues and 6 medical care domains. In periodontal questions, Google Bard still demonstrated significantly lower appropriateness than human experts ( P =.04). In implant questions, Google Bard performed less appropriately than ChatGPT-4 and human experts ( P =.03 and P =.01, respectively) and less comprehensively than ChatGPT-3.5 and 4 ( P =.02 and P =.05, respectively). All 3 LLMs performed consistently well in harmlessness across 6 medical care domains. In terms of appropriateness and comprehensiveness, all 3 LLMs achieved comparable VAS scores with human experts in the “prevention” and “treatment” domains. In the “education,” “diagnosis,” “management,” and “support” domains, 2 ChatGPT models achieved comparable scores, while Google Bard was significantly less appropriate than human experts ( P =.01, P =.02, P =.04, and P =.03, respectively). Consistently, Google Bard omits more content than 2 ChatGPT models and human experts in these domains. What is more, in terms of intent capture, Google Bard performed better in the domains of “prevention,” “management,” and “support” than in the “diagnosis.” Detailed subanalyses are shown in Multimedia Appendices 3 and 4 .

Stability Evaluation Results

Table 3 presents the stability evaluation results. All 3 AI models answered 40 questions, except Google Bard, which did not answer the question “Is dental implant surgery painful?” in 2 of 3 attempts. ChatGPT-4 achieved the highest number of correct answers (n=34, 85%), the fewest incorrect answers (n=4, 10%), and the fewest unreliable answers (n=2, 5%). ChatGPT-3.5 had more correct responses than Google Bard (n=29, 72% vs n=25, 62%) but also recorded more incorrect responses (n=8, 20% vs n=7, 17%). Moreover, ChatGPT-3.5 had fewer unreliable responses compared to Google Bard (n=3, 7% vs n=8, 20%).

Principal Findings

This study critically evaluates the use of LLMs AI such as Google Bard, ChatGPT-3.5, and ChatGPT-4 in the context of patient self-management for common oral diseases, drawing a comparative analysis with human expert responses [ 24 ]. Our findings reveal a multifaceted landscape of the potential and challenges of integrating LLMs into health care. The results underscore a promising future for AI chatbots to assist clinical workflows by augmenting patient education and patient-clinician communication around common oral disease queries with comparable accuracy, harmfulness, and comprehensiveness to human experts. However, they also highlight existing challenges that necessitate ongoing optimization strategies since even the most capable models have some inaccuracy and inconsistency.

Comparison to Prior Work

In the comprehensive evaluation of the 3 LLMs, ChatGPT-4 emerged as the superior model, consistent with prior assessments in various medical domains [ 10 , 25 , 26 ]. This superior performance is likely attributable to its substantially larger training data set, continuous architectural enhancements, and notable advancements in language processing, contextual comprehension, and advanced reasoning skills [ 20 ]. These improvements are crucial in health care applications, where the precision and relevance of information are critical. Interestingly, despite ChatGPT-4 showing greater stability, no significant differences were observed between ChatGPT-4 and ChatGPT-3.5 in dentist and patient evaluations. Given that ChatGPT-4 is a premium version not universally accessible, ChatGPT-3.5 holds significant value for broader applications.

In assessments spanning both periodontal and implant-related issues as well as a range of medical domains, Google Bard consistently demonstrated the least effective performance in addressing basic oral disease queries, particularly within the “diagnosis” domain. Notably, Google Bard’s tendency to avoid questions about dental implant surgery pain, in contrast to ChatGPT’s consistent responsiveness, might reflect differing strategies in risk management. However, in terms of readability, an important criterion for nonmedical users’ educational materials, Google Bard outperformed even human experts. This aligns with prior studies assessing LLMs’ readability and agrees with the impact of different training data and preprocessing methods on LLMs’ readability [ 27 , 28 ].

Future Directions

Moreover, all 3 LLM chatbots performed similarly in providing harmless responses. In the context of medical conversation, these AI models consistently encouraged patients to seek professional medical advice, underscoring the irreplaceable role of human expertise diagnosis and treatment. However, the results of the lay user evaluation warrant caution, as they show that AI models were comparable to human experts in intent capture and helpfulness. This ambiguous distinction poses a paradox. On one hand, it suggests user acceptance in AI-provided information, underscoring their capability to effectively address user inquiries. On the other hand, it discreetly underscores a potential risk: the lay users’ limited ability to judge the accuracy of complex medical information, which might inadvertently lead to AI disseminating misconceptions or inappropriate guidance. This underscores the critical need to address the ethical consideration of integrating AI in health care [ 29 , 30 ]. It is essential to clearly define the responsibilities and risks associated with using AI in patient education and in facilitating patient-clinician communication.

The observed performance differences among the AI models, influenced by factors like diverse training data sets and algorithmic updates, combined with the lay evaluations, emphasize the importance of customizing and continually updating LLMs for oral health care. Tailoring AI to meet specific oral health needs and maintaining current medical standards are crucial to ensure safe and accurate patient support.

Strengths and Limitations

LLMs demonstrate varied performances across different medical fields, which can be attributed to the varying depth of available web-based data on each topic. It is imperative to thoroughly evaluate their efficacy across diverse medical topics. In comparison to systemic diseases, using LLMs for basic oral health conditions offers substantial benefits. First, the narrower scope of oral diseases renders personalized oral hygiene advice and disease risk prediction via AI more viable. Second, the relative simplicity of oral structures, combined with AI’s advanced image recognition capabilities, facilitates the more feasible identification and analysis of oral imagery, thus aiding early-stage problem detection. This research underscores the potential of using AI to provide individualized oral health guidance to patients, which could significantly broaden their access to medical knowledge, reduce health care expenses, enhance medical efficiency, lower public health costs, balance medical resource distribution, and relieve national economic burdens.

To our knowledge, this is the first study to evaluate the application of current LLMs comprehensively and rigorously in basic oral diseases. The robust experimental design and the implementation of blinding largely reduce evaluator bias, ensuring the validity of the results. However, this study is not without limitations. First, its methodology, based on simulated question-and-answer scenarios, does not fully replicate real-world clinical interactions. Future research should involve actual patient interactions for more accurate assessment. Second, the performance of the LLM largely depends on the quality of the prompt guiding the model, highlighting the necessity for further research in this area. With the currently rapid evolution of LLMs, there is a critical need to develop specialized chatbots with medical expertise, combining the strengths of current LLMs for health care applications. Currently, integrating medical professionals seems to be the most effective strategy for optimizing AI applications in health care.

Conclusions

LLMs, particularly ChatGPT-4, demonstrate promising potential in providing patient-centric information for common oral diseases. Variations in performance underscore the need for ongoing refinement and ethical considerations. Future studies should explore strategies to integrate LLMs effectively in health care settings, ensuring their safe and effective use in patient care.

Conflicts of Interest

None declared.

Question list.

Evaluation axes.

Subanalysis results of periodontal and implant-related queries.

Subanalysis results of 6 medical care domains.

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Abbreviations

Edited by G Eysenbach, T de Azevedo Cardoso; submitted 27.12.23; peer-reviewed by L Weinert, L Zhu, W Cao; comments to author 26.02.24; revised version received 04.03.24; accepted 19.03.24; published 25.04.24.

©Xiaolei Lv, Xiaomeng Zhang, Yuan Li, Xinxin Ding, Hongchang Lai, Junyu Shi. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 25.04.2024.

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