ArticleDigital health
Assessing AI efficacy in medical knowledge tests: A study using Taiwan's internal medicine exam questions from 2020 to 2023.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Large Language Models in Neurological Practice: Real-World Study.Journal of medical Internet research · 2025Article
- Performance evaluation of GPT-4o on South Korean national exams for building mechanical equipment maintenance.Scientific reports · 2025Article
- Evaluating the performance of GPT-3.5, GPT-4, and GPT-4o in the Chinese National Medical Licensing Examination.Scientific reports · 2025Article
- Large Language Models for Pre-mediation Counseling in Medical Disputes: A Comparative Evaluation against Human Experts.Healthcare informatics research · 2025Article
- The Surge of Artificial Intelligence (AI) in Scientific Writing: Who Will Hold the Rudder, You or AI?Hip & pelvis · 2024Article
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Authors and funding
6 authors.
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Abstract
Background: The aim of this study is to evaluate the ability of generative artificial intelligence (AI) models to handle specialized medical knowledge and problem-solving in a formal examination context. Methods: This research utilized internal medicine exam questions provided by the Taiwan Internal Medicine Society from 2020 to 2023, testing three AI models: GPT-4o, Claude_3.5 Sonnet, and Gemini Advanced models. Rejected queries for Gemini Advanced were translated into French for resubmission. Performance was assessed using IBM SPSS Statistics 26, with accuracy percentages calculated and statistical analyses such as Pearson correlation and analysis of variance (ANOVA) performed to gauge AI efficacy. Results: GPT-4o's top annual score was 86.25 in 2022, with an average of 81.97. Claude_3.5 Sonnet reached a peak score of 88.13 in 2021 and 2022, averaging 84.85, while Gemini Advanced lagged with an average score of 69.84. In specific specialties, Claude_3.5 Sonnet scored highest in Psychiatry (100%) and Nephrology (97.26%), with GPT-4o performing similarly well in Hematology & oncology (97.10%) and Nephrology (94.52%). Gemini's best scores were in Psychiatry (86.96%) and Hematology & Oncology (82.76%). Gemini Advanced models struggled with Neurology, scoring below 60%. Additionally, all models performed better on text-based questions than on image-based ones, without significant differences. Claude 3 Opus scored highest on COVID-19-related questions at 89.29%, followed by GPT-4o at 75.00% and Gemini Advanced at 67.86%. Conclusions: AI models showed varied proficiency across medical specialties and question types. GPT-4o demonstrated higher image-based correction rates. Claude_3.5 Sonnet generally and consistently outperformed others, highlighting significant potential for AI in assisting medical education.
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