Performance of Large Language Models (ChatGPT, Bing Search, and Google Bard) in Solving Case Vignettes in Physiology
Open accessAbstract
Background Large language models (LLMs) have emerged as powerful tools capable of processing and generating human-like text. These LLMs, such as ChatGPT (OpenAI Incorporated, Mission District, San Francisco, United States), Google Bard (Alphabet Inc., CA, US), and Microsoft Bing (Microsoft Corporation, WA, US), have been applied across various domains, demonstrating their potential to assist in solving complex tasks and improving information accessibility. However, their application in solving case vignettes in physiology has not been explored. This study aimed to assess the performance of three LLMs, namely, ChatGPT (3.5; free research version), Google Bard (Experiment), and Microsoft Bing (precise), in answering cases vignettes in Physiology. Methods This cross-sectional study was conducted in July 2023. A total of 77 case vignettes in physiology were prepared by two physiologists and were validated by two other content experts. These cases were presented to each LLM, and their responses were collected. Two physiologists independently rated the answers provided by the LLMs based on their accuracy. The ratings were measured on a scale from 0 to 4 according to the structure of the observed learning outcome (pre-structural = 0, uni-structural = 1, multi-structural = 2, relational = 3, extended-abstract). The scores among the LLMs were compared by Friedman's test and inter-observer agreement was checked by the intraclass correlation coefficient (ICC). Results The overall scores for ChatGPT, Bing, and Bard in the study, with a total of 77 cases, were found to be 3.19±0.3, 2.15±0.6, and 2.91±0.5, respectively, p<0.0001. Hence, ChatGPT 3.5 (free version) obtained the highest score, Bing (Precise) had the lowest score, and Bard (Experiment) fell in between the two in terms of performance. The average ICC values for ChatGPT, Bing, and Bard were 0.858 (95% CI: 0.777 to 0.91, p<0.0001), 0.975 (95% CI: 0.961 to 0.984, p<0.0001), and 0.964 (95% CI: 0.944 to 0.977, p<0.0001), respectively. Conclusion ChatGPT outperformed Bard and Bing in answering case vignettes in physiology. Hence, students and teachers may think about choosing LLMs for their educational purposes accordingly for case-based learning in physiology. Further exploration of their capabilities is needed for adopting those in medical education and support for clinical decision-making.
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Several studies examined the performance of LLMs on various medical exams; comparisons between older and newer versions indicated that newer versions exhibited better accuracy; one study comparing ChatGPT and Google's Bard in answering multiple-choice questionnaires related to endocrinology and diabetes found ChatGPT to be slightly superior, but both models lacked up-to-date information; another
“Several studies examined the performance of LLMs on various medical exams [97–99]. Comparisons between older and newer versions indicated that newer versions exhibited better accuracy, highlighting the need for ongoing assessment of AI tools in medical education [100–102]. One study comparing ChatGPT and Google’s Bard in answering multiple-choice questionnaires (MCQs) related to endocrinology and”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [99]
Split into 3 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredChatGPT's precision accelerates task completion, helping students grasp medical knowledge in practical scenarios more effectively.
“ChatGPT’s precision accelerates task completion, helping students grasp medical knowledge in practical scenarios more effectively [78].”
From Applications, Challenges, and Prospects of Generative Artificial Intelligence Empowering Medical Education: Scoping Review (2025) · cites this paper as [78]
The Conclusions report that ChatGPT offered accurate, contextually relevant answers to physiology case vignettes, and the Discussion says LLMs can provide quick answers and help students review concepts. The study did not measure faster task completion or whether students understood medical knowledge more effectively in practical scenarios; those outcomes are presented more strongly than the source supports. No page numbers are available in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredMicrosoft Bing achieved top scores alongside GPT-4 in medical licensing MCQ exams.
“Microsoft Bing achieved top scores alongside GPT-4 in medical licensing MCQ exams;”
From Applications, Challenges, and Prospects of Generative Artificial Intelligence Empowering Medical Education: Scoping Review (2025) · cites this paper as [78]
The Discussion (page unavailable) reports that another study found Bing Chat performed best on Royal College of Ophthalmologists fellowship exams. It does not report Bing achieving top scores alongside GPT-4 on medical licensing MCQs. In this paper’s own physiology case-vignette study, Bing scored lowest, as reported in the Abstract (page unavailable). (LLM, conf=0.82)
Experimental — accuracy not yet measuredChatGPT-3.5 excels in simulating doctor-patient conversations, generating exam questions, and providing personalized learning support.
“ChatGPT-3.5 excels in simulating doctor-patient conversations, generating exam questions, and providing personalized learning support;”
From Applications, Challenges, and Prospects of Generative Artificial Intelligence Empowering Medical Education: Scoping Review (2025) · cites this paper as [78]
The Introduction says LLMs such as ChatGPT can help teachers and students generate questions, and the Discussion describes interactive learning and support for self-directed learning. Neither section establishes that ChatGPT-3.5 excels at simulating doctor–patient conversations or providing personalized learning support; the study tested its answers to physiology case vignettes, not those uses (no page numbers available). (LLM, conf=0.85)
Experimental — accuracy not yet measuredIn a cross-sectional study of 77 physiology case vignettes, ChatGPT scored highest at 3.19 (SD 0.3), outperforming Bard (2.91, SD 0.5) and Bing (2.15, SD 0.6) with P<.001.
“A cross-sectional study tested 77 physiology case vignettes (covering diverse physiological and pathophysiological scenarios, designed for undergraduates) on ChatGPT 3.5, Google Bard, and Microsoft Bing. Rated by two physiologists on a 0‐4 scale, ChatGPT scored highest at 3.19 (SD 0.3), outperforming Bard (2.91, SD 0.5) and Bing (2.15, SD 0.6) with P<.001.”
From Applications, Challenges, and Prospects of Generative Artificial Intelligence Empowering Medical Education: Scoping Review (2025) · cites this paper as [78]
In physiological case analysis, GAI offers precise responses and contextually relevant feedback.
“For instance, in physiological case analysis, GAI offers precise responses and contextually relevant feedback.”
From Applications, Challenges, and Prospects of Generative Artificial Intelligence Empowering Medical Education: Scoping Review (2025) · cites this paper as [78]
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Enhanced Healthcare AI Assistance Through Blockchain-Integrated Data Access
Althobaiti, M., & Jun, M. (2025) · IEEE International Conference on Blockchain, Smart Healthcare and Emerging Technologies (SmartBlock4Health) · doi:10.1109/smartblock4health64843.2025.11189387 · Cited by 1 · see citing works
“Furthermore, [23] evaluated the performance of ChatGPT (3.5), Google Bard, and Microsoft Bing in answering 77 physiology case vignettes.”
InfluentialEray, O., & Polat, L. N. Ö. (2026) · Journal of Intelligent Systems Theory and Applications · doi:10.38016/jista.1831503 · Cited by 1 · see citing works
Sallam, M., Barakat, M. M., & Sallam, M. (2023) · Preprints.org · doi:10.31222/osf.io/qrsph · Cited by 1 · see citing works
Ye, Z., Lai, H., Sun, M., Huang, J., Liu, J., Xia, D., Zhao, W., Liu, J., & Ge, L. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4756038
Reza, R. B., Mahmud, M. R., & Ahmed, S. M. Z. (2024) · Science & Technology Libraries · doi:10.1080/0194262x.2024.2413558
“While several studies, including Dhanvijay et al. (2023) and Lim et al. (2023a, 2023b) found ChatGPT exhibited superior performance than its competitors, other studies showed that ChatGPT performed lower in specific contexts (Dao 2023; Frosolini et al. 2024).”
Comparison between ChatGPT 3.0 and Google Gemini Regarding Medicine KnowledgePreprint
Hassan, M. M., & Nohor, N. (2024) · Research Square · doi:10.21203/rs.3.rs-5423066/v1
Embracing Large Language Models for Adult Life Support Learning
Patel, S., & Patel, R. B. (2024) · Cureus · doi:10.7759/cureus.75961
“In other studies of medical physiology, ChatGPT secured around 75% [24,25].”
Hossain, M. J., Hassan, M. M., Maruf, M. F. I., & Saha, A. (2025) · Research Square · doi:10.21203/rs.3.rs-6740202/v1
Clustering Techniques for Survey Response in Education Using Large Language Models
Villaceran, E. C., & Cabije, A. T. A. (2025) · International Conference on Artificial Intelligence for Sustainable Innovation (AI-SI) · doi:10.1109/ai-si66213.2025.11341525
“These factors include rethinking assessment and examinations (RAE), developing holistic student competencies/generic skills (GS), preparing students for the AI-driven workplace (PS), and encouraging a balanced approach to AI adoption (BA) [5], [12], [13].”
Güner, S. G., Tan, Z., & GÜLPINAR, S. (2026) · BMC Nursing · doi:10.1186/s12912-026-04702-z
Quantifying Similarities: Oncology Documents from Google Bard and ChatGPT
Naveed, M. S. (2023) · International Journal of Innovations in Science and Technology · doi:10.33411/ijist/602
Bagley, R. (2026) · Frontiers in Veterinary Science · doi:10.3389/fvets.2026.1940970
- Authors
- Dhanvijay, A. K. D., Pinjar, M. J., Dhokane, N., Sorte, S. R., Kumari, A., & Mondal, H.
- Affiliations
- All India Institute of Medical Sciences, Deoghar (IN)
- Corresponding author
- Mondal, Himel
- Venue
- Cureus
- Published
- 2023-08-04
- Volume
- 15
- Issue
- 8
- Pages
- e42972-e42972
- ISSN
- 2168-8184 (ISSN-L)
- PMC
- PMC10475852
- OpenAlex
- W4385900159
- Cited by
- 59
- References
- 16
- Citations by year
- 2026: 4 · 2025: 18 · 2024: 29 · 2023: 8
- FWCI
- 2.36
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Cureus (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Keywords
- large language models, ChatGPT, Google Bard, Bing Chat, medical education, LLM evaluation, answer accuracy, Bloom's taxonomy, interobserver agreement, artificial intelligence in medical education, clinical reasoning assessment, ChatGPT-3.5
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