ChatGPT Conquers the Saudi Medical Licensing Exam: Exploring the Accuracy of Artificial Intelligence in Medical Knowledge Assessment and Implications for Modern Medical Education

BackgroundThe application of artificial intelligence (AI) in education is undergoing rapid advancements, with models such as ChatGPT-4 showing potential in medical education. This study aims to evaluate the proficiency of ChatGPT-4 in answering Saudi Medical Licensing Exam (SMLE) questions.Methodolo...

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Veröffentlicht in:Curēus (Palo Alto, CA) CA), 2023-09, Vol.15 (9), p.e45043-e45043
Hauptverfasser: Aljindan, Fahad K, Al Qurashi, Abdullah A, Albalawi, Ibrahim Abdullah S, Alanazi, Abeer Mohammed M, Aljuhani, Hussam Abdulkhaliq M, Falah Almutairi, Faisal, Aldamigh, Omar A, Halawani, Ibrahim R, K. Zino Alarki, Subhi M
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Sprache:eng
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Zusammenfassung:BackgroundThe application of artificial intelligence (AI) in education is undergoing rapid advancements, with models such as ChatGPT-4 showing potential in medical education. This study aims to evaluate the proficiency of ChatGPT-4 in answering Saudi Medical Licensing Exam (SMLE) questions.MethodologyA dataset of 220 questions across four medical disciplines was used. The model was trained using a specific code to answer the questions accurately, and its performance was assessed using key performance indicators, difficulty level, and exam sections.ResultsChatGPT-4 demonstrated an overall accuracy of 88.6%. It showed high proficiency with Easy and Average questions, but accuracy decreased for Hard questions. Performance was consistent across all disciplines, indicating a broad knowledge base. However, an error analysis revealed areas for further refinement, particularly with category (Option) A questions across all sections.ConclusionsThis study underscores the potential of ChatGPT-4 as an AI-assisted tool in medical education, demonstrating high proficiency in answering SMLE questions. Future research is recommended to expand the scope of training and evaluation as well as to enhance the model’s performance on complex clinical questions.
ISSN:2168-8184
2168-8184
DOI:10.7759/cureus.45043