Journal of Educational Evaluation for Health Professions

Research articles

Refusal-aware evaluation of frontier AI models available in June 2026 using the Japanese National License Examination for Pharmacists: a comparative study: Hiroyasu Sato, Katsuhiko Ogasawara, Hidehiko Sakurai; J Educ Eval Health Prof. 2026;23:29. Published online September 3, 2026; DOI: https://doi.org/10.3352/jeehp.2026.23.29 [Epub ahead of print]

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Abstract PDF: Purpose
Conventional single-run accuracy may be insufficient for evaluating frontier generative artificial intelligence (AI) models when safety-related refusals occur. This 4-model benchmark examined the need for repeated, refusal-aware evaluation using the Japanese National License Examination for Pharmacists (JNLEP).
Methods
ChatGPT GPT-5.5, Gemini 3.5 Flash, Claude Opus 4.8, and Claude Fable 5 were evaluated using all 345 questions from the 107th JNLEP. The original Japanese questions, including image-containing items, were submitted through application programming interfaces (APIs) in 3 independent runs. Refusals were treated as incorrect when overall accuracy was calculated. For Fable 5, accuracy excluding refusals, refusal rate, refusal consistency across runs, the subject-wise distribution of refusals, and system-assigned refusal categories were also evaluated.
Results
The mean overall accuracies were 98.7% for GPT-5.5, 98.3% for Gemini 3.5 Flash, 96.1% for Claude Opus 4.8, and 70.3% for Claude Fable 5. Fable 5 had a mean refusal rate of 29.0%, whereas its mean accuracy excluding refusals was 99.0%. Among the 345 items, 93 were refused in all 3 runs, 14 were refused inconsistently across runs, and 238 were never refused. All 300 refusal responses were assigned to the bio category. Refusals were most frequent in Biology (90.0%) and Pharmacology (69.2%) but uncommon in Practice (2.5%).
Conclusion
Near-saturation benchmark performance coexisted with frequent and partly run-dependent refusals in a safeguard-equipped model. Overall accuracy, accuracy excluding refusals, refusal rate, and refusal consistency describe complementary aspects of performance; therefore, repeated, refusal-aware evaluation is needed to interpret frontier AI models in pharmacy education.

Practicing pediatric interviews with parents through conversational AI-based virtual patients: an observational study in Spanish undergraduate medical education: César Fernández, Francisco Sánchez-Ferrer, María Asunción Vicente; J Educ Eval Health Prof. 2026;23:27. Published online August 31, 2026; DOI: https://doi.org/10.3352/jeehp.2026.23.27 [Epub ahead of print]

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Abstract PDF Supplementary Material: Purpose
To evaluate students’ acceptance of training via artificial intelligence (AI)-based virtual patients (VPs) in pediatrics; to analyze the main factors that affect student satisfaction; and to compare student satisfaction with that reported in the literature.
Methods
Two observational studies were carried out. Study S1 analyzed students’ interactions with the platform and study S2 analyzed their answers to a final questionnaire. All students enrolled in the “Pediatrics II and Pediatric Surgery” course were invited to participate during a continuous session on May 7, 2025. Study S1 analyzed whether the case/session order, the number of interactions, the time spent, and student gender were associated with student satisfaction (by fitting a linear mixed-effects model); and also performed a qualitative analysis of students’ open-ended comments. Study S2 analyzed the influence of demographic data on students’ feedback (by fitting proportional odds ordinal logistic regression models and linear models). All experimental data, code and results are available.
Results
Concerning study S1, 70 students participated. Platform rating was high (mean=9.04, standard deviation=1.09) and was positively associated with the number of interactions with the VPs (P=0.038, β=0.022 [0.001–0.043]). Concerning study S2, 60 students participated. No statistically significant influence of demographics was found either for answers to individual questionnaire items or for grouped answers.
Conclusion
Student acceptance of training with AI-based VPs was supported by the satisfaction outcomes measured and by comparison with previous studies. However, further research is needed to confirm these findings.

Development and psychometric validation of the Thai AI Literacy Scale for Nursing Students in Thailand: a methodological study: Wannaporn Jongchidklang, Supalak Phonphithak, Porntep Amornritvanich; J Educ Eval Health Prof. 2026;23:22. Published online August 4, 2026; DOI: https://doi.org/10.3352/jeehp.2026.23.22 [Epub ahead of print]

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Abstract PDF: Purpose
This study aimed to develop and validate the Thai AI Literacy Scale for Nursing Students (TAILS-NS).
Methods
A cross-sectional study was conducted across multiple nursing institutions in Thailand from March to May 2026 to address the absence of a validated artificial intelligence (AI) literacy instrument for nursing students in Thai or Southeast Asian contexts. A total of 410 nursing students participated, yielding a response rate of 94.5%. The TAILS-NS was developed through item generation, expert content-validity assessment using the item-objective congruence index, and a 2-phase pilot study, resulting in a 40-item instrument comprising a 15-item knowledge test and 25 Likert-scale items across 6 domains. Exploratory factor analysis using maximum likelihood estimation and confirmatory factor analysis using the weighted least squares mean and variance-adjusted estimator, as well as internal consistency, convergent validity, and discriminant validity, were assessed. An independent validation sample (n=157) was recruited for cross-validation confirmatory factor analysis. Raw data are available as a supplement.
Results
Exploratory factor analysis supported a 6-factor structure: AI awareness, skills, ethics and professionalism, positive attitude, AI anxiety, and readiness. Confirmatory factor analysis showed acceptable model fit, although the root mean square error of approximation (RMSEA) indicated marginal fit (comparative fit index [CFI]=0.919, Tucker-Lewis index [TLI]=0.906, RMSEA=0.090). Reliability of the knowledge subscale was acceptable (Kuder-Richardson Formula 20=0.758). Internal consistency was excellent (α=0.810–0.934; total α=0.974; ω=0.989). Average variance extracted exceeded 0.50 for all factors, supporting convergent validity. Most heterotrait-monotrait ratios were below 0.90, supporting discriminant validity. Cross-validation confirmed factorial replicability (CFI=0.917, TLI=0.904).
Conclusion
The TAILS-NS provides evidence of validity and reliability for measuring AI literacy among Thai nursing students and may serve as a standardized tool for curriculum evaluation. Future studies should expand the AI anxiety subscale and explore cross-cultural applicability in Southeast Asian nursing contexts.

Review

Analysis of digital twin applications in nursing practice and education: a scoping review: A Reum Lim, Hyun Kyoung Kim; J Educ Eval Health Prof. 2026;23:13. Published online June 9, 2026; DOI: https://doi.org/10.3352/jeehp.2026.23.13

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Abstract PDF Supplementary Material: This scoping review examined research applying digital twins in nursing practice and education and summarized their application domains, methods, outcomes, and implications. A human digital twin is a virtual health replica modeled from real-world data. This study followed the 5-stage scoping review process proposed by Arksey and O’Malley. Two researchers independently conducted the literature search without restrictions on publication year. From April 1 to 15, 2026, the Cochrane Library, PubMed, Embase, CINAHL, ERIC, and RISS databases were searched, and 15 studies were ultimately included. Digital twin applications were identified in 3 major domains: clinical practice and patient-centered care, education and training, and decision-making and workflow management. Application methods and outcomes varied according to technological implementation and included (1) modeling and data-driven prediction, (2) development of immersive learning and practice-training environments, and (3) system integration and decision-support frameworks. In clinical settings, multimodal patient data can be analyzed using artificial intelligence and machine learning to generate a virtual persona resembling the patient, thereby facilitating real-time personalized nursing care and self-management. In educational settings, digital twins can provide realistic and safe learning environments that enhance training effectiveness. Digital twins show substantial potential to advance predictive and personalized nursing in both clinical practice and education. Their data-driven capabilities are expected to contribute to innovative applications in future nursing practice and educational environments.

Brief report

Large language model-generated versus teacher-written objective structured clinical examination stations for medical students: a blinded comparative pilot study: Piotr Szychowiak, Jonathan Wong-So, Hélène Messet, Mélanie Faure, Isaure Breteau, Simon Jamard, François Barbier, Maxime Desgrouas; J Educ Eval Health Prof. 2026;23:9. Published online May 26, 2026; DOI: https://doi.org/10.3352/jeehp.2026.23.9

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Abstract PDF Supplementary Material: Developing objective structured clinical examination (OSCE) stations is time-consuming for medical teachers. We aimed to evaluate the ability of a large language model (LLM) to generate ready-to-use OSCE stations. Five OSCE stations generated by the LLM GPT-4o were evaluated by 7 expert assessors using a 5-point Likert scale and compared with 5 teacher-written stations targeting similar learning objectives. A station was considered to be of good quality if most assessors responded “agree” or “strongly agree” to the statement “The station is good enough to be used by students.” All teacher-written stations were rated as being of good quality, compared with only one GPT-4o-generated station. The LLM produced adequate clinical scenarios when reference knowledge was provided and tasks were clearly ordered, but it failed to generate reliable assessment grids. Careful review by teachers remained essential. GPT-4o failed to consistently produce fully ready-to-use OSCE stations.

Reviews

The impact of artificial intelligence-driven simulation on the development of non-technical skills in medical education: a systematic review: Sana Loubbairi, Yasmine El Moussaoui, Laila Lahlou, Imad Chakri, Hicham Nassik; J Educ Eval Health Prof. 2025;22:37. Published online November 24, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.37

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Abstract PDF Supplementary Material

Purpose
Artificial intelligence (AI)-driven simulation is an emerging approach in healthcare education that enhances learning effectiveness. This review examined its impact on the development of non-technical skills among medical learners.
Methods
Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a systematic review was conducted using the following databases: Web of Science, ScienceDirect, Scopus, and PubMed. The quality of the included studies was assessed using the Mixed Methods Appraisal Tool. The protocol was previously registered in PROSPERO (CRD420251038024).
Results
Of the 1,442 studies identified in the initial search, 20 met the inclusion criteria, involving 2,535 participants. The simulators varied considerably, ranging from platforms built on symbolic AI methods to social robots powered by computational AI. Among the 15 AI-driven simulators, 10 used ChatGPT or its variants as virtual patients. Several studies evaluated multiple non-technical skills simultaneously. Communication and clinical reasoning were the most frequently assessed skills, appearing in 12 and 6 studies, respectively, which generally reported positive outcomes. Improvements were also noted in decision-making, empathy, self-confidence, critical thinking, and problem-solving. In contrast, emotional regulation, assessed in a single study, showed no significant difference. Notably, none of the studies examined reflection, reflective practice, teamwork, or leadership.
Conclusion
AI-driven simulation shows substantial potential for enhancing non-technical skills in medical education, particularly communication and clinical reasoning. However, its effects on several other non-technical skills remain unclear. Given heterogeneity in study designs and outcome measures, these findings should be interpreted cautiously. These considerations highlight the need for further research to support integrating this innovative approach into medical curricula.

Citations

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The Effect of Infection Control Session on Nursing Students' Knowledge and Compliance with Standard Precautions at Hassan College of Nursing, Swat
Ahmad Ullah, Hammad Ullah Khan, Zia Ullah Khan, Numan Khan, Shah Hussain
medtigo Journal of Medicine.2026;[Epub] CrossRef
Empowering Surgical Training through Artificial Intelligence: A Cross-Sectional Study on Residents’ Acceptance and Perceived Usefulness of AI-Based Simulation
Sami Ur Rahman, Kulsoom Nadir, Muhammad Ilyas, Mehar Nigar, Anwar Khan, Abdur Rahman, Shah Hussain
medtigo Journal of Medicine.2026;[Epub] CrossRef
Advances in evaluating and delivering nontechnical skills training: The use of simulation, robotics, artificial intelligence and virtual reality
Ravanth Baskaran, Aditya Singh, Bhaskar Kumar Somani
Current Opinion in Urology.2026; 36(5): 485. CrossRef
La formación en medicina interna en la era de la inteligencia artificial
J. García-Alegría, D. Ruiz-Hidalgo, M. Rodríguez-Carballeira
Revista Clínica Española.2026; : 502609. CrossRef
Effectiveness of a “5E+AI” teaching model on science communication in medical students: a contribution to global health literacy
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Training in internal medicine in the age of artificial intelligence
J. García-Alegría, D. Ruiz-Hidalgo, M. Rodríguez-Carballeira
Revista Clínica Española (English Edition).2026; : 502609. CrossRef

Performance of large language models in medical licensing examinations: a systematic review and meta-analysis: Haniyeh Nouri, Abdollah Mahdavi, Ali Abedi, Alireza Mohammadnia, Mahnaz Hamedan, Masoud Amanzadeh; J Educ Eval Health Prof. 2025;22:36. Published online November 18, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.36

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Abstract PDF Supplementary Material

Purpose
This study systematically evaluates and compares the performance of large language models (LLMs) in answering medical licensing examination questions. By conducting subgroup analyses based on language, question format, and model type, this meta-analysis aims to provide a comprehensive overview of LLM capabilities in medical education and clinical decision-making.
Methods
This systematic review, registered in PROSPERO and following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, searched MEDLINE (PubMed), Scopus, and Web of Science for relevant articles published up to February 1, 2025. The search strategy included Medical Subject Headings (MeSH) terms and keywords related to (“ChatGPT” OR “GPT” OR “LLM variants”) AND (“medical licensing exam*” OR “medical exam*” OR “medical education” OR “radiology exam*”). Eligible studies evaluated LLM accuracy on medical licensing examination questions. Pooled accuracy was estimated using a random-effects model, with subgroup analyses by LLM type, language, and question format. Publication bias was assessed using Egger’s regression test.
Results
This systematic review identified 2,404 studies. After removing duplicates and excluding irrelevant articles through title and abstract screening, 36 studies were included after full-text review. The pooled accuracy was 72% (95% confidence interval, 70.0% to 75.0%) with high heterogeneity (I2=99%, P<0.001). Among LLMs, GPT-4 achieved the highest accuracy (81%), followed by Bing (79%), Claude (74%), Gemini/Bard (70%), and GPT-3.5 (60%) (P=0.001). Performance differences across languages (range, 62% in Polish to 77% in German) were not statistically significant (P=0.170).
Conclusion
LLMs, particularly GPT-4, can match or exceed medical students’ examination performance and may serve as supportive educational tools. However, due to variability and the risk of errors, they should be used cautiously as complements rather than replacements for traditional learning methods.

Citations

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Effective prompt design for large language models in clinical practice
Steven Callens
Acta Clinica Belgica.2026; 81(2): 118. CrossRef
Examination of Gemini's ability to answer anatomical questions: An overview
D. Chytas, G. Noussios, M.-K. Kaseta, D. Chrysikos, A.V. Vasiliadis, C. Lyrtzis, T. Troupis
Morphologie.2026; 110(369): 101118. CrossRef
Evaluación comparativa de modelos de inteligencia artificial de última generación frente a psiquiatras humanos en el examen nacional de subespecialidad en Perú: un estudio transversal
Javier A. Flores-Cohaila, Jeff Huarcaya-Victoria, Cesar Copaja-Corzo
Educación Médica.2026; 27(3): 101179. CrossRef
ChatGPT vs Claude: Scoping Review with ☸️SAIMSARA

SAIMSARA Journal.2026;[Epub] CrossRef
PeruMedQA: A Stress Evaluation Using Ten Large Language Models to Answer Medical Exams
Rodrigo M. Carrillo-Larco
Medical Science Educator.2026; 36(3): 1091. CrossRef
Evaluating the accuracy and communication quality of large language models in Ewing sarcoma: a comparative analysis of ChatGPT, Claude, Gemini, DeepSeek, and Grok
Cihan Ünyılmaz
Frontiers in Pediatrics.2026;[Epub] CrossRef
The Promises and Perils of Clinical Decision Support Artificial Intelligence
Jorge Cervantes, Bhavya Vashi
The Clinical Teacher.2026;[Epub] CrossRef
NASA TLX workload profiles of multimodal artificial intelligence models and dental students during objective structured practical dental examinations
Sanaa N. Al-Haj Ali, Ra’fat I. Farah
Discover Education.2026;[Epub] CrossRef
Artificial Intelligence and Sleep
Logan Douglas Schneider, John Hernandez, Conor Heneghan
Neurologic Clinics.2026;[Epub] CrossRef
Generative artificial intelligence in medical education: from knowledge assessment to clinical reasoning and professional competence
Renxian Xie, Beien Zhang, Lifeng Xiao
Frontiers in Medicine.2026;[Epub] CrossRef

Prompt engineering for single-best-answer multiple-choice questions in licensing examinations: a narrative review with a case study involving the Korean Medical Licensing Examination: Bokyoung Kim, Junseok Kang, Min-Young Kim, Jihyun Ahn; J Educ Eval Health Prof. 2025;22:34. Published online October 27, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.34

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Abstract PDF Supplementary Material

The emergence of large language models (LLMs) has generated growing interest in their potential applications for medical assessment and item development. This practice-oriented narrative review examines the potential of LLMs, particularly ChatGPT, for generating and validating single-best-answer multiple-choice questions in health professions licensing examinations, using a Korean Medical Licensing Examination (KMLE)-focused case perspective. We frame LLMs as human-in-the-loop tools rather than replacements for high-stakes testing. Recent applications of LLMs in assessment were reviewed, including prompting strategies such as few-shot, multi-stage, and chain-of-thought methods, as well as retrieval-augmented generation (RAG) to align outputs with exam blueprints. Approaches to enforcing formatting rules, checklist-based self-validation, and iterative refinement were analyzed for their role in supporting item development. Findings indicate that LLMs can perform near passing thresholds on high-stakes exams and assist with grading and feedback tasks. Prompt engineering enhances structural fidelity and clinical plausibility, while human oversight remains critical for accuracy, cultural appropriateness, and psychometric defensibility. The emerging multimodal generation of images, audio, and video suggests the feasibility of new item formats, provided robust validation safeguards are implemented. The most effective approach is a human-in-the-loop workflow that leverages artificial intelligence efficiency while embedding expert judgment, psychometric evaluation, and ethical governance. This practice-oriented roadmap—integrating strategic prompt selection, RAG-based blueprint alignment, rigorous validation gates, and KMLE-specific formatting—offers an implementable and methodologically defensible approach for licensing examinations.

Citations

Citations to this article as recorded by

Generative artificial intelligence and large language models in competency-based medical education: applications, challenges, and future directions
In Hwa Jeong, Heeyoung Kim, Hyunyong Hwang
Kosin Medical Journal.2026; 41(2): 114. CrossRef

Research articles

Performance of GPT-4o and o1-Pro on United Kingdom Medical Licensing Assessment-style items: a comparative study: Behrad Vakili, Aadam Ahmad, Mahsa Zolfaghari; J Educ Eval Health Prof. 2025;22:30. Published online October 10, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.30

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Abstract PDF Supplementary Material: Purpose
Large language models (LLMs) such as ChatGPT, and their potential to support autonomous learning for licensing exams like the UK Medical Licensing Assessment (UKMLA), are of growing interest. However, empirical evaluations of artificial intelligence (AI) performance against the UKMLA standard remain limited.
Methods
We evaluated the performance of 2 recent ChatGPT versions, GPT-4o and o1-Pro, on a curated set of 374 UKMLA-style single-best-answer items spanning diverse medical specialties. Statistical comparisons using McNemar’s test assessed the significance of differences between the 2 models. Specialties were analyzed to identify domain-specific variation. In addition, 20 image-based items were evaluated.
Results
GPT-4o achieved an accuracy of 88.8%, while o1-Pro achieved 93.0%. McNemar’s test revealed a statistically significant difference in favor of o1-Pro. Across specialties, both models demonstrated excellent performance in surgery, psychiatry, and infectious diseases. Notable differences arose in dermatology, respiratory medicine, and imaging, where o1-Pro consistently outperformed GPT-4o. Nevertheless, isolated weaknesses in general practice were observed. The analysis of image-based items showed 75% accuracy for GPT-4o and 90% for o1-Pro (P=0.25).
Conclusion
ChatGPT shows strong potential as an adjunct learning tool for UKMLA preparation, with both models achieving scores above the calculated pass mark. This underscores the promise of advanced AI models in medical education. However, specialty-specific inconsistencies suggest AI tools should complement, rather than replace, traditional study methods.

Performance of ChatGPT-4 on the French Board of Plastic Reconstructive and Aesthetic Surgery written exam: a descriptive study: Emma Dejean-Bouyer, Anoujat Kanlagna, François Thuau, Pierre Perrot, Ugo Lancien; J Educ Eval Health Prof. 2025;22:27. Published online September 30, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.27

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Abstract PDF Supplementary Material: Purpose
This study aims to evaluate the performance of Chat Generative Pre-Trained Transformer 4 (ChatGPT-4) on the French Board of Plastic, Reconstructive, and Aesthetic Surgery written examination and to assess its role as a supplementary resource in helping residents prepare for the qualification examination in plastic surgery.
Methods
This descriptive study evaluated ChatGPT-4’s performance on 213 items from the October 2024 French Board of Plastic, Reconstructive, and Aesthetic Surgery written examination. Responses were assessed for accuracy, logical reasoning, internal and external information use, and were categorized for fallacies by independent reviewers. Statistical analyses included chi-square tests and Fisher’s exact test for significance.
Results
ChatGPT-4 answered all questions across the 10 modules, achieving an overall accuracy rate of 77.5%. The model applied logical reasoning in 98.1% of the questions, utilized internal information in 94.4%, and incorporated external information in 91.1%.
Conclusion
ChatGPT-4 performs satisfactorily on the French Board of Plastic, Reconstructive, and Aesthetic Surgery written examination. Its accuracy met the minimum passing standards for the exam. While responses generally align with expected knowledge, careful verification remains necessary, particularly for questions involving image interpretation. As artificial intelligence continues to evolve, ChatGPT-4 is expected to become an increasingly reliable tool for medical education. At present, it remains a valuable resource for assisting plastic surgery residents in their training.

Comparing generative artificial intelligence platforms and nursing student performance on a women’s health nursing examination in Korea: a Rasch model approach: Eun Jeong Ko, Tae Kyung Lee, Geum Hee Jeong; J Educ Eval Health Prof. 2025;22:23. Published online September 5, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.23

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Abstract PDF Supplementary Material

Purpose
This psychometric study aimed to compare the ability parameter estimates of generative artificial intelligence (AI) platforms with those of nursing students on a 50-item women’s health nursing examination at Hallym University, Korea, using the Rasch model. It also sought to estimate item difficulty parameters and evaluate AI performance across varying difficulty levels.
Methods
The exam, consisting of 39 multiple-choice items and 11 true/false items, was administered to 111 fourth-year nursing students in June 2023. In December 2024, 6 generative AI platforms (GPT-4o, ChatGPT free version, Claude.ai, Clova X, Mistral.ai, Google Gemini) completed the same items. The responses were analyzed using the Rasch model to estimate the ability and difficulty parameters. Unidimensionality was verified by the Dimensionality Evaluation to Enumerate Contributing Traits (DETECT), and analyses were conducted using the R packages irtQ and TAM.
Results
The items satisfied unidimensionality (DETECT=–0.16). Item difficulty parameter estimates ranged from –3.87 to 1.96 logits (mean=–0.61), with a mean difficulty index of 0.79. Examinees’ ability parameter estimates ranged from –0.71 to 3.15 logits (mean=1.17). GPT-4o, ChatGPT free version, and Claude.ai outperformed the median student ability (1.09 logits), scoring 2.68, 2.34, and 2.34, respectively, while Clova X, Mistral.ai, and Google Gemini exhibited lower scores (0.20, –0.12, 0.80). The test information curve peaked below θ=0, indicating suitability for examinees with low to average ability.
Conclusion
Advanced generative AI platforms approximated the performance of high-performing students, but outcomes varied. The Rasch model effectively evaluated AI competency, supporting its potential utility for future AI performance assessments in nursing education.

Citations

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Eroding scholarly integrity: Confronting the misuse of generative AI in nursing education
Kechi Iheduru-Anderson
Nurse Education Today.2026; 164: 107145. CrossRef

Comparison between GPT-4 and human raters in grading pharmacy students’ exam responses in Malaysia: a cross-sectional study: Wuan Shuen Yap, Pui San Saw, Li Ling Yeap, Shaun Wen Huey Lee, Wei Jin Wong, Ronald Fook Seng Lee; J Educ Eval Health Prof. 2025;22:20. Published online July 28, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.20

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Abstract PDF Supplementary Material: Purpose
Manual grading is time-consuming and prone to inconsistencies, prompting the exploration of generative artificial intelligence tools such as GPT-4 to enhance efficiency and reliability. This study investigated GPT-4’s potential in grading pharmacy students’ exam responses, focusing on the impact of optimized prompts. Specifically, it evaluated the alignment between GPT-4 and human raters, assessed GPT-4’s consistency over time, and determined its error rates in grading pharmacy students’ exam responses.
Methods
We conducted a comparative study using past exam responses graded by university-trained raters and by GPT-4. Responses were randomized before evaluation by GPT-4, accessed via a Plus account between April and September 2024. Prompt optimization was performed on 16 responses, followed by evaluation of 3 prompt delivery methods. We then applied the optimized approach across 4 item types. Intraclass correlation coefficients and error analyses were used to assess consistency and agreement between GPT-4 and human ratings.
Results
GPT-4’s ratings aligned reasonably well with human raters, demonstrating moderate to excellent reliability (intraclass correlation coefficient=0.617–0.933), depending on item type and the optimized prompt. When stratified by grade bands, GPT-4 was less consistent in marking high-scoring responses (Z=–5.71–4.62, P<0.001). Overall, despite achieving substantial alignment with human raters in many cases, discrepancies across item types and a tendency to commit basic errors necessitate continued educator involvement to ensure grading accuracy.
Conclusion
With optimized prompts, GPT-4 shows promise as a supportive tool for grading pharmacy students’ exam responses, particularly for objective tasks. However, its limitations—including errors and variability in grading high-scoring responses—require ongoing human oversight. Future research should explore advanced generative artificial intelligence models and broader assessment formats to further enhance grading reliability.

Performance of large language models on Thailand’s national medical licensing examination: a cross-sectional study: Prut Saowaprut, Romen Samuel Wabina, Junwei Yang, Lertboon Siriwat; J Educ Eval Health Prof. 2025;22:16. Published online May 12, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.16

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Abstract PDF Supplementary Material

Purpose
This study aimed to evaluate the feasibility of general-purpose large language models (LLMs) in addressing inequities in medical licensure exam preparation for Thailand’s National Medical Licensing Examination (ThaiNLE), which currently lacks standardized public study materials.
Methods
We assessed 4 multi-modal LLMs (GPT-4, Claude 3 Opus, Gemini 1.0/1.5 Pro) using a 304-question ThaiNLE Step 1 mock examination (10.2% image-based), applying deterministic API configurations and 5 inference repetitions per model. Performance was measured via micro- and macro-accuracy metrics compared against historical passing thresholds.
Results
All models exceeded passing scores, with GPT-4 achieving the highest accuracy (88.9%; 95% confidence interval, 88.7–89.1), surpassing Thailand’s national average by more than 2 standard deviations. Claude 3.5 Sonnet (80.1%) and Gemini 1.5 Pro (72.8%) followed hierarchically. Models demonstrated robustness across 17 of 20 medical domains, but variability was noted in genetics (74.0%) and cardiovascular topics (58.3%). While models demonstrated proficiency with images (Gemini 1.0 Pro: +9.9% vs. text), text-only accuracy remained superior (GPT-4o: 90.0% vs. 82.6%).
Conclusion
General-purpose LLMs show promise as equitable preparatory tools for ThaiNLE Step 1. However, domain-specific knowledge gaps and inconsistent multi-modal integration warrant refinement before clinical deployment.

Citations

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Is artificial intelligence getting better at anatomy? A two‐year review of ChatGPT's free public versions
Bahattin Paslı, Ceren Günenç Beşer
Anatomical Sciences Education.2026; 19(8): 1279. CrossRef
The performance of ChatGPT and other large language models on multiple‐choice questions in biomedical disciplines: A meta‐analysis
Colleen M. Cheverko, Volodymyr Mavrych, Olena Bolgova, Fathima Raahima Riyas Mohamed, Jennifer Westrick, Lorena Juarez, Emily Rush, Kathryn A. Solka, Alison F. Doubleday, Jessica N. Byram, Robert Becker, Victoria Gomez, Brenda K. Anak Ganeng, Leslie A. Ho
Anatomical Sciences Education.2026; 19(9): 1556. CrossRef
Performance of GPT-4o and o1-Pro on United Kingdom Medical Licensing Assessment-style items: a comparative study
Behrad Vakili, Aadam Ahmad, Mahsa Zolfaghari
Journal of Educational Evaluation for Health Professions.2025; 22: 30. CrossRef
Large Language Models for the National Radiological Technologist Licensure Examination in Japan: Cross-Sectional Comparative Benchmarking and Evaluation of Model-Generated Items Study
Toshimune Ito, Toru Ishibashi, Tatsuya Hayashi, Shinya Kojima, Kazumi Sogabe
JMIR Medical Education.2025; 11: e81807. CrossRef
Technologies, opportunities, challenges, and future directions for integrating generative artificial intelligence into medical education: a narrative review
Junseok Kang, Jihyun Ahn
Ewha Medical Journal.2025; 48(4): e53. CrossRef

Educational/Faculty development material

The role of large language models in the peer-review process: opportunities and challenges for medical journal reviewers and editors: Jisoo Lee, Jieun Lee, Jeong-Ju Yoo; J Educ Eval Health Prof. 2025;22:4. Published online January 16, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.4

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Abstract PDF Supplementary Material

The peer review process ensures the integrity of scientific research. This is particularly important in the medical field, where research findings directly impact patient care. However, the rapid growth of publications has strained reviewers, causing delays and potential declines in quality. Generative artificial intelligence, especially large language models (LLMs) such as ChatGPT, may assist researchers with efficient, high-quality reviews. This review explores the integration of LLMs into peer review, highlighting their strengths in linguistic tasks and challenges in assessing scientific validity, particularly in clinical medicine. Key points for integration include initial screening, reviewer matching, feedback support, and language review. However, implementing LLMs for these purposes will necessitate addressing biases, privacy concerns, and data confidentiality. We recommend using LLMs as complementary tools under clear guidelines to support, not replace, human expertise in maintaining rigorous peer review standards.

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Research article

Effectiveness of ChatGPT-4o in developing continuing professional development plans for graduate radiographers: a descriptive study: Minh Chau, Elio Stefan Arruzza, Kelly Spuur; J Educ Eval Health Prof. 2024;21:34. Published online November 18, 2024; DOI: https://doi.org/10.3352/jeehp.2024.21.34

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Abstract PDF Supplementary Material

Purpose
This study evaluates the use of ChatGPT-4o in creating tailored continuing professional development (CPD) plans for radiography students, addressing the challenge of aligning CPD with Medical Radiation Practice Board of Australia (MRPBA) requirements. We hypothesized that ChatGPT-4o could support students in CPD planning while meeting regulatory standards.
Methods
A descriptive, experimental design was used to generate 3 unique CPD plans using ChatGPT-4o, each tailored to hypothetical graduate radiographers in varied clinical settings. Each plan followed MRPBA guidelines, focusing on computed tomography specialization by the second year. Three MRPBA-registered academics assessed the plans using criteria of appropriateness, timeliness, relevance, reflection, and completeness from October 2024 to November 2024. Ratings underwent analysis using the Friedman test and intraclass correlation coefficient (ICC) to measure consistency among evaluators.
Results
ChatGPT-4o generated CPD plans generally adhered to regulatory standards across scenarios. The Friedman test indicated no significant differences among raters (P=0.420, 0.761, and 0.807 for each scenario), suggesting consistent scores within scenarios. However, ICC values were low (–0.96, 0.41, and 0.058 for scenarios 1, 2, and 3), revealing variability among raters, particularly in timeliness and completeness criteria, suggesting limitations in the ChatGPT-4o’s ability to address individualized and context-specific needs.
Conclusion
ChatGPT-4o demonstrates the potential to ease the cognitive demands of CPD planning, offering structured support in CPD development. However, human oversight remains essential to ensure plans are contextually relevant and deeply reflective. Future research should focus on enhancing artificial intelligence’s personalization for CPD evaluation, highlighting ChatGPT-4o’s potential and limitations as a tool in professional education.

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Educational/Faculty development material

The performance of ChatGPT-4.0o in medical imaging evaluation: a cross-sectional study: Elio Stefan Arruzza, Carla Marie Evangelista, Minh Chau; J Educ Eval Health Prof. 2024;21:29. Published online October 31, 2024; DOI: https://doi.org/10.3352/jeehp.2024.21.29

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Abstract PDF Supplementary Material

This study investigated the performance of ChatGPT-4.0o in evaluating the quality of positioning in radiographic images. Thirty radiographs depicting a variety of knee, elbow, ankle, hand, pelvis, and shoulder projections were produced using anthropomorphic phantoms and uploaded to ChatGPT-4.0o. The model was prompted to provide a solution to identify any positioning errors with justification and offer improvements. A panel of radiographers assessed the solutions for radiographic quality based on established positioning criteria, with a grading scale of 1–5. In only 20% of projections, ChatGPT-4.0o correctly recognized all errors with justifications and offered correct suggestions for improvement. The most commonly occurring score was 3 (9 cases, 30%), wherein the model recognized at least 1 specific error and provided a correct improvement. The mean score was 2.9. Overall, low accuracy was demonstrated, with most projections receiving only partially correct solutions. The findings reinforce the importance of robust radiography education and clinical experience.

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Research articles

GPT-4o’s competency in answering the simulated written European Board of Interventional Radiology exam compared to a medical student and experts in Germany and its ability to generate exam items on interventional radiology: a descriptive study: Sebastian Ebel, Constantin Ehrengut, Timm Denecke, Holger Gößmann, Anne Bettina Beeskow; J Educ Eval Health Prof. 2024;21:21. Published online August 20, 2024; DOI: https://doi.org/10.3352/jeehp.2024.21.21

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361 Download
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Abstract PDF Supplementary Material

Purpose
This study aimed to determine whether ChatGPT-4o, a generative artificial intelligence (AI) platform, was able to pass a simulated written European Board of Interventional Radiology (EBIR) exam and whether GPT-4o can be used to train medical students and interventional radiologists of different levels of expertise by generating exam items on interventional radiology.
Methods
GPT-4o was asked to answer 370 simulated exam items of the Cardiovascular and Interventional Radiology Society of Europe (CIRSE) for EBIR preparation (CIRSE Prep). Subsequently, GPT-4o was requested to generate exam items on interventional radiology topics at levels of difficulty suitable for medical students and the EBIR exam. Those generated items were answered by 4 participants, including a medical student, a resident, a consultant, and an EBIR holder. The correctly answered items were counted. One investigator checked the answers and items generated by GPT-4o for correctness and relevance. This work was done from April to July 2024.
Results
GPT-4o correctly answered 248 of the 370 CIRSE Prep items (67.0%). For 50 CIRSE Prep items, the medical student answered 46.0%, the resident 42.0%, the consultant 50.0%, and the EBIR holder 74.0% correctly. All participants answered 82.0% to 92.0% of the 50 GPT-4o generated items at the student level correctly. For the 50 GPT-4o items at the EBIR level, the medical student answered 32.0%, the resident 44.0%, the consultant 48.0%, and the EBIR holder 66.0% correctly. All participants could pass the GPT-4o-generated items for the student level; while the EBIR holder could pass the GPT-4o-generated items for the EBIR level. Two items (0.3%) out of 150 generated by the GPT-4o were assessed as implausible.
Conclusion
GPT-4o could pass the simulated written EBIR exam and create exam items of varying difficulty to train medical students and interventional radiologists.

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Performance of GPT-3.5 and GPT-4 on standardized urology knowledge assessment items in the United States: a descriptive study: Max Samuel Yudovich, Elizaveta Makarova, Christian Michael Hague, Jay Dilip Raman; J Educ Eval Health Prof. 2024;21:17. Published online July 8, 2024; DOI: https://doi.org/10.3352/jeehp.2024.21.17

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Abstract PDF Supplementary Material

Purpose
This study aimed to evaluate the performance of Chat Generative Pre-Trained Transformer (ChatGPT) with respect to standardized urology multiple-choice items in the United States.
Methods
In total, 700 multiple-choice urology board exam-style items were submitted to GPT-3.5 and GPT-4, and responses were recorded. Items were categorized based on topic and question complexity (recall, interpretation, and problem-solving). The accuracy of GPT-3.5 and GPT-4 was compared across item types in February 2024.
Results
GPT-4 answered 44.4% of items correctly compared to 30.9% for GPT-3.5 (P<0.00001). GPT-4 (vs. GPT-3.5) had higher accuracy with urologic oncology (43.8% vs. 33.9%, P=0.03), sexual medicine (44.3% vs. 27.8%, P=0.046), and pediatric urology (47.1% vs. 27.1%, P=0.012) items. Endourology (38.0% vs. 25.7%, P=0.15), reconstruction and trauma (29.0% vs. 21.0%, P=0.41), and neurourology (49.0% vs. 33.3%, P=0.11) items did not show significant differences in performance across versions. GPT-4 also outperformed GPT-3.5 with respect to recall (45.9% vs. 27.4%, P<0.00001), interpretation (45.6% vs. 31.5%, P=0.0005), and problem-solving (41.8% vs. 34.5%, P=0.56) type items. This difference was not significant for the higher-complexity items.
Conclusions
ChatGPT performs relatively poorly on standardized multiple-choice urology board exam-style items, with GPT-4 outperforming GPT-3.5. The accuracy was below the proposed minimum passing standards for the American Board of Urology’s Continuing Urologic Certification knowledge reinforcement activity (60%). As artificial intelligence progresses in complexity, ChatGPT may become more capable and accurate with respect to board examination items. For now, its responses should be scrutinized.

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Review

Opportunities, challenges, and future directions of large language models, including ChatGPT in medical education: a systematic scoping review: Xiaojun Xu, Yixiao Chen, Jing Miao; J Educ Eval Health Prof. 2024;21:6. Published online March 15, 2024; DOI: https://doi.org/10.3352/jeehp.2024.21.6

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Abstract PDF Supplementary Material

Background
ChatGPT is a large language model (LLM) based on artificial intelligence (AI) capable of responding in multiple languages and generating nuanced and highly complex responses. While ChatGPT holds promising applications in medical education, its limitations and potential risks cannot be ignored.
Methods
A scoping review was conducted for English articles discussing ChatGPT in the context of medical education published after 2022. A literature search was performed using PubMed/MEDLINE, Embase, and Web of Science databases, and information was extracted from the relevant studies that were ultimately included.
Results
ChatGPT exhibits various potential applications in medical education, such as providing personalized learning plans and materials, creating clinical practice simulation scenarios, and assisting in writing articles. However, challenges associated with academic integrity, data accuracy, and potential harm to learning were also highlighted in the literature. The paper emphasizes certain recommendations for using ChatGPT, including the establishment of guidelines. Based on the review, 3 key research areas were proposed: cultivating the ability of medical students to use ChatGPT correctly, integrating ChatGPT into teaching activities and processes, and proposing standards for the use of AI by medical students.
Conclusion
ChatGPT has the potential to transform medical education, but careful consideration is required for its full integration. To harness the full potential of ChatGPT in medical education, attention should not only be given to the capabilities of AI but also to its impact on students and teachers.

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Research articles

ChatGPT (GPT-4) passed the Japanese National License Examination for Pharmacists in 2022, answering all items including those with diagrams: a descriptive study: Hiroyasu Sato, Katsuhiko Ogasawara; J Educ Eval Health Prof. 2024;21:4. Published online February 28, 2024; DOI: https://doi.org/10.3352/jeehp.2024.21.4

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Abstract PDF Supplementary Material

Purpose
The objective of this study was to assess the performance of ChatGPT (GPT-4) on all items, including those with diagrams, in the Japanese National License Examination for Pharmacists (JNLEP) and compare it with the previous GPT-3.5 model’s performance.
Methods
The 107th JNLEP, conducted in 2022, with 344 items input into the GPT-4 model, was targeted for this study. Separately, 284 items, excluding those with diagrams, were entered into the GPT-3.5 model. The answers were categorized and analyzed to determine accuracy rates based on categories, subjects, and presence or absence of diagrams. The accuracy rates were compared to the main passing criteria (overall accuracy rate ≥62.9%).
Results
The overall accuracy rate for all items in the 107th JNLEP in GPT-4 was 72.5%, successfully meeting all the passing criteria. For the set of items without diagrams, the accuracy rate was 80.0%, which was significantly higher than that of the GPT-3.5 model (43.5%). The GPT-4 model demonstrated an accuracy rate of 36.1% for items that included diagrams.
Conclusion
Advancements that allow GPT-4 to process images have made it possible for LLMs to answer all items in medical-related license examinations. This study’s findings confirm that ChatGPT (GPT-4) possesses sufficient knowledge to meet the passing criteria.

Citations

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Applications and potential of ChatGPT in dentistry: Scoping review of research perspectives
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Performance of ChatGPT‐3.5 and ChatGPT‐4o in the Japanese National Dental Examination
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Information amount, accuracy, and relevance of generative artificial intelligence platforms’ answers regarding learning objectives of medical arthropodology evaluated in English and Korean queries in December 2023: a descriptive study: Hyunju Lee, Soobin Park; J Educ Eval Health Prof. 2023;20:39. Published online December 28, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.39

6,531 View
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Abstract PDF Supplementary Material

Purpose
This study assessed the performance of 6 generative artificial intelligence (AI) platforms on the learning objectives of medical arthropodology in a parasitology class in Korea. We examined the AI platforms’ performance by querying in Korean and English to determine their information amount, accuracy, and relevance in prompts in both languages.
Methods
From December 15 to 17, 2023, 6 generative AI platforms—Bard, Bing, Claude, Clova X, GPT-4, and Wrtn—were tested on 7 medical arthropodology learning objectives in English and Korean. Clova X and Wrtn are platforms from Korean companies. Responses were evaluated using specific criteria for the English and Korean queries.
Results
Bard had abundant information but was fourth in accuracy and relevance. GPT-4, with high information content, ranked first in accuracy and relevance. Clova X was 4th in amount but 2nd in accuracy and relevance. Bing provided less information, with moderate accuracy and relevance. Wrtn’s answers were short, with average accuracy and relevance. Claude AI had reasonable information, but lower accuracy and relevance. The responses in English were superior in all aspects. Clova X was notably optimized for Korean, leading in relevance.
Conclusion
In a study of 6 generative AI platforms applied to medical arthropodology, GPT-4 excelled overall, while Clova X, a Korea-based AI product, achieved 100% relevance in Korean queries, the highest among its peers. Utilizing these AI platforms in classrooms improved the authors’ self-efficacy and interest in the subject, offering a positive experience of interacting with generative AI platforms to question and receive information.

Citations

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Review

Application of artificial intelligence chatbots, including ChatGPT, in education, scholarly work, programming, and content generation and its prospects: a narrative review: Tae Won Kim; J Educ Eval Health Prof. 2023;20:38. Published online December 27, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.38

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1,578 Download
69 Web of Science
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Abstract PDF Supplementary Material

This study aims to explore ChatGPT’s (GPT-3.5 version) functionalities, including reinforcement learning, diverse applications, and limitations. ChatGPT is an artificial intelligence (AI) chatbot powered by OpenAI’s Generative Pre-trained Transformer (GPT) model. The chatbot’s applications span education, programming, content generation, and more, demonstrating its versatility. ChatGPT can improve education by creating assignments and offering personalized feedback, as shown by its notable performance in medical exams and the United States Medical Licensing Exam. However, concerns include plagiarism, reliability, and educational disparities. It aids in various research tasks, from design to writing, and has shown proficiency in summarizing and suggesting titles. Its use in scientific writing and language translation is promising, but professional oversight is needed for accuracy and originality. It assists in programming tasks like writing code, debugging, and guiding installation and updates. It offers diverse applications, from cheering up individuals to generating creative content like essays, news articles, and business plans. Unlike search engines, ChatGPT provides interactive, generative responses and understands context, making it more akin to human conversation, in contrast to conventional search engines’ keyword-based, non-interactive nature. ChatGPT has limitations, such as potential bias, dependence on outdated data, and revenue generation challenges. Nonetheless, ChatGPT is considered to be a transformative AI tool poised to redefine the future of generative technology. In conclusion, advancements in AI, such as ChatGPT, are altering how knowledge is acquired and applied, marking a shift from search engines to creativity engines. This transformation highlights the increasing importance of AI literacy and the ability to effectively utilize AI in various domains of life.

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Brief report

ChatGPT (GPT-3.5) as an assistant tool in microbial pathogenesis studies in Sweden: a cross-sectional comparative study: Catharina Hultgren, Annica Lindkvist, Volkan Özenci, Sophie Curbo; J Educ Eval Health Prof. 2023;20:32. Published online November 22, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.32

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Abstract PDF Supplementary Material

ChatGPT (GPT-3.5) has entered higher education and there is a need to determine how to use it effectively. This descriptive study compared the ability of GPT-3.5 and teachers to answer questions from dental students and construct detailed intended learning outcomes. When analyzed according to a Likert scale, we found that GPT-3.5 answered the questions from dental students in a similar or even more elaborate way compared to the answers that had previously been provided by a teacher. GPT-3.5 was also asked to construct detailed intended learning outcomes for a course in microbial pathogenesis, and when these were analyzed according to a Likert scale they were, to a large degree, found irrelevant. Since students are using GPT-3.5, it is important that instructors learn how to make the best use of it both to be able to advise students and to benefit from its potential.

Citations

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Research articles

Performance of ChatGPT, Bard, Claude, and Bing on the Peruvian National Licensing Medical Examination: a cross-sectional study: Betzy Clariza Torres-Zegarra, Wagner Rios-Garcia, Alvaro Micael Ñaña-Cordova, Karen Fatima Arteaga-Cisneros, Xiomara Cristina Benavente Chalco, Marina Atena Bustamante Ordoñez, Carlos Jesus Gutierrez Rios, Carlos Alberto Ramos Godoy, Kristell Luisa Teresa Panta Quezada, Jesus Daniel Gutierrez-Arratia, Javier Alejandro Flores-Cohaila; J Educ Eval Health Prof. 2023;20:30. Published online November 20, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.30

10,526 View
306 Download
45 Web of Science
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Abstract PDF Supplementary Material

Purpose
We aimed to describe the performance and evaluate the educational value of justifications provided by artificial intelligence chatbots, including GPT-3.5, GPT-4, Bard, Claude, and Bing, on the Peruvian National Medical Licensing Examination (P-NLME).
Methods
This was a cross-sectional analytical study. On July 25, 2023, each multiple-choice question (MCQ) from the P-NLME was entered into each chatbot (GPT-3, GPT-4, Bing, Bard, and Claude) 3 times. Then, 4 medical educators categorized the MCQs in terms of medical area, item type, and whether the MCQ required Peru-specific knowledge. They assessed the educational value of the justifications from the 2 top performers (GPT-4 and Bing).
Results
GPT-4 scored 86.7% and Bing scored 82.2%, followed by Bard and Claude, and the historical performance of Peruvian examinees was 55%. Among the factors associated with correct answers, only MCQs that required Peru-specific knowledge had lower odds (odds ratio, 0.23; 95% confidence interval, 0.09–0.61), whereas the remaining factors showed no associations. In assessing the educational value of justifications provided by GPT-4 and Bing, neither showed any significant differences in certainty, usefulness, or potential use in the classroom.
Conclusion
Among chatbots, GPT-4 and Bing were the top performers, with Bing performing better at Peru-specific MCQs. Moreover, the educational value of justifications provided by the GPT-4 and Bing could be deemed appropriate. However, it is essential to start addressing the educational value of these chatbots, rather than merely their performance on examinations.

Citations

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Medical students’ patterns of using ChatGPT as a feedback tool and perceptions of ChatGPT in a Leadership and Communication course in Korea: a cross-sectional study: Janghee Park; J Educ Eval Health Prof. 2023;20:29. Published online November 10, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.29

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Abstract PDF Supplementary Material

Purpose
This study aimed to analyze patterns of using ChatGPT before and after group activities and to explore medical students’ perceptions of ChatGPT as a feedback tool in the classroom.
Methods
The study included 99 2nd-year pre-medical students who participated in a “Leadership and Communication” course from March to June 2023. Students engaged in both individual and group activities related to negotiation strategies. ChatGPT was used to provide feedback on their solutions. A survey was administered to assess students’ perceptions of ChatGPT’s feedback, its use in the classroom, and the strengths and challenges of ChatGPT from May 17 to 19, 2023.
Results
The students responded by indicating that ChatGPT’s feedback was helpful, and revised and resubmitted their group answers in various ways after receiving feedback. The majority of respondents expressed agreement with the use of ChatGPT during class. The most common response concerning the appropriate context of using ChatGPT’s feedback was “after the first round of discussion, for revisions.” There was a significant difference in satisfaction with ChatGPT’s feedback, including correctness, usefulness, and ethics, depending on whether or not ChatGPT was used during class, but there was no significant difference according to gender or whether students had previous experience with ChatGPT. The strongest advantages were “providing answers to questions” and “summarizing information,” and the worst disadvantage was “producing information without supporting evidence.”
Conclusion
The students were aware of the advantages and disadvantages of ChatGPT, and they had a positive attitude toward using ChatGPT in the classroom.

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Efficacy and limitations of ChatGPT as a biostatistical problem-solving tool in medical education in Serbia: a descriptive study: Aleksandra Ignjatović, Lazar Stevanović; J Educ Eval Health Prof. 2023;20:28. Published online October 16, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.28

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Abstract PDF Supplementary Material

Purpose
This study aimed to assess the performance of ChatGPT (GPT-3.5 and GPT-4) as a study tool in solving biostatistical problems and to identify any potential drawbacks that might arise from using ChatGPT in medical education, particularly in solving practical biostatistical problems.
Methods
ChatGPT was tested to evaluate its ability to solve biostatistical problems from the Handbook of Medical Statistics by Peacock and Peacock in this descriptive study. Tables from the problems were transformed into textual questions. Ten biostatistical problems were randomly chosen and used as text-based input for conversation with ChatGPT (versions 3.5 and 4).
Results
GPT-3.5 solved 5 practical problems in the first attempt, related to categorical data, cross-sectional study, measuring reliability, probability properties, and the t-test. GPT-3.5 failed to provide correct answers regarding analysis of variance, the chi-square test, and sample size within 3 attempts. GPT-4 also solved a task related to the confidence interval in the first attempt and solved all questions within 3 attempts, with precise guidance and monitoring.
Conclusion
The assessment of both versions of ChatGPT performance in 10 biostatistical problems revealed that GPT-3.5 and 4’s performance was below average, with correct response rates of 5 and 6 out of 10 on the first attempt. GPT-4 succeeded in providing all correct answers within 3 attempts. These findings indicate that students must be aware that this tool, even when providing and calculating different statistical analyses, can be wrong, and they should be aware of ChatGPT’s limitations and be careful when incorporating this model into medical education.

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Brief report

Comparing ChatGPT’s ability to rate the degree of stereotypes and the consistency of stereotype attribution with those of medical students in New Zealand in developing a similarity rating test: a methodological study: Chao-Cheng Lin, Zaine Akuhata-Huntington, Che-Wei Hsu; J Educ Eval Health Prof. 2023;20:17. Published online June 12, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.17

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Abstract PDF Supplementary Material

Learning about one’s implicit bias is crucial for improving one’s cultural competency and thereby reducing health inequity. To evaluate bias among medical students following a previously developed cultural training program targeting New Zealand Māori, we developed a text-based, self-evaluation tool called the Similarity Rating Test (SRT). The development process of the SRT was resource-intensive, limiting its generalizability and applicability. Here, we explored the potential of ChatGPT, an automated chatbot, to assist in the development process of the SRT by comparing ChatGPT’s and students’ evaluations of the SRT. Despite results showing non-significant equivalence and difference between ChatGPT’s and students’ ratings, ChatGPT’s ratings were more consistent than students’ ratings. The consistency rate was higher for non-stereotypical than for stereotypical statements, regardless of rater type. Further studies are warranted to validate ChatGPT’s potential for assisting in SRT development for implementation in medical education and evaluation of ethnic stereotypes and related topics.

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Review

Can an artificial intelligence chatbot be the author of a scholarly article?: Ju Yoen Lee; J Educ Eval Health Prof. 2023;20:6. Published online February 27, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.6

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At the end of 2022, the appearance of ChatGPT, an artificial intelligence (AI) chatbot with amazing writing ability, caused a great sensation in academia. The chatbot turned out to be very capable, but also capable of deception, and the news broke that several researchers had listed the chatbot (including its earlier version) as co-authors of their academic papers. In response, Nature and Science expressed their position that this chatbot cannot be listed as an author in the papers they publish. Since an AI chatbot is not a human being, in the current legal system, the text automatically generated by an AI chatbot cannot be a copyrighted work; thus, an AI chatbot cannot be an author of a copyrighted work. Current AI chatbots such as ChatGPT are much more advanced than search engines in that they produce original text, but they still remain at the level of a search engine in that they cannot take responsibility for their writing. For this reason, they also cannot be authors from the perspective of research ethics.

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Brief report

Are ChatGPT’s knowledge and interpretation ability comparable to those of medical students in Korea for taking a parasitology examination?: a descriptive study: Sun Huh; J Educ Eval Health Prof. 2023;20:1. Published online January 11, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.1

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Abstract PDF Supplementary Material

This study aimed to compare the knowledge and interpretation ability of ChatGPT, a language model of artificial general intelligence, with those of medical students in Korea by administering a parasitology examination to both ChatGPT and medical students. The examination consisted of 79 items and was administered to ChatGPT on January 1, 2023. The examination results were analyzed in terms of ChatGPT’s overall performance score, its correct answer rate by the items’ knowledge level, and the acceptability of its explanations of the items. ChatGPT’s performance was lower than that of the medical students, and ChatGPT’s correct answer rate was not related to the items’ knowledge level. However, there was a relationship between acceptable explanations and correct answers. In conclusion, ChatGPT’s knowledge and interpretation ability for this parasitology examination were not yet comparable to those of medical students in Korea.

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Review

What should medical students know about artificial intelligence in medicine?: Seong Ho Park, Kyung-Hyun Do, Sungwon Kim, Joo Hyun Park, Young-Suk Lim; J Educ Eval Health Prof. 2019;16:18. Published online July 3, 2019; DOI: https://doi.org/10.3352/jeehp.2019.16.18

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Abstract PDF Supplementary Material

Artificial intelligence (AI) is expected to affect various fields of medicine substantially and has the potential to improve many aspects of healthcare. However, AI has been creating much hype, too. In applying AI technology to patients, medical professionals should be able to resolve any anxiety, confusion, and questions that patients and the public may have. Also, they are responsible for ensuring that AI becomes a technology beneficial for patient care. These make the acquisition of sound knowledge and experience about AI a task of high importance for medical students. Preparing for AI does not merely mean learning information technology such as computer programming. One should acquire sufficient knowledge of basic and clinical medicines, data science, biostatistics, and evidence-based medicine. As a medical student, one should not passively accept stories related to AI in medicine in the media and on the Internet. Medical students should try to develop abilities to distinguish correct information from hype and spin and even capabilities to create thoroughly validated, trustworthy information for patients and the public.

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Advances in Medical Education and Practice.2022; Volume 13: 927. CrossRef
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Research article

An expert-led and artificial intelligence system-assisted tutoring course to improve the confidence of Chinese medical interns in suturing and ligature skills: a prospective pilot study: Ying-Ying Yang, Boaz Shulruf; J Educ Eval Health Prof. 2019;16:7. Published online April 10, 2019; DOI: https://doi.org/10.3352/jeehp.2019.16.7

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Abstract PDF Supplementary Material

Purpose
Lack of confidence in suturing/ligature skills due to insufficient practice and assessments is common among novice Chinese medical interns. This study aimed to improve the skill acquisition of medical interns through a new intervention program.
Methods
In addition to regular clinical training, expert-led or expert-led plus artificial intelligence (AI) system tutoring courses were implemented during the first 2 weeks of the surgical block. Interns could voluntarily join the regular (no additional tutoring), expert-led tutoring, or expert-led+AI tutoring groups freely. In the regular group, interns (n=25) did not receive additional tutoring. The expert-led group received 3-hour expert-led tutoring and in-training formative assessments after 2 practice sessions. After a similar expert-led course, the expert-led+AI group (n=23) practiced and assessed their skills on an AI system. Through a comparison with the internal standard, the system automatically recorded and evaluated every intern’s suturing/ligature skills. In the expert-led+AI group, performance and confidence were compared between interns who participated in 1, 2, or 3 AI practice sessions.
Results
The end-of-surgical block objective structured clinical examination (OSCE) performance and self-assessed confidence in suturing/ligature skills were highest in the expert-led+AI group. In comparison with the expert-led group, the expert-led+AI group showed similar performance in the in-training assessment and greater improvement in the end-of-surgical block OSCE. In the expert-led+AI group, the best performance and highest post-OSCE confidence were noted in those who engaged in 3 AI practice sessions.
Conclusion
This pilot study demonstrated the potential value of incorporating an additional expert-led+AI system–assisted tutoring course into the regular surgical curriculum.

Citations

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