Assessing the Performance of Chatbots on the Taiwan Psychiatry Licensing Examination Using the Rasch Model

Abstract

BACKGROUND/OBJECTIVES: The potential and limitations of chatbots in medical education and clinical decision support, particularly in specialized fields like psychiatry, remain unknown. By using the Rasch model, our study aimed to evaluate the performance of various state-of-the-art chatbots on psychiatry licensing exam questions to explore their strengths and weaknesses. METHODS: We assessed the performance of 22 leading chatbots, selected based on LMArena benchmark rankings, using 100 multiple-choice questions from the 2024 Taiwan psychiatry licensing examination, a nationally standardized test required for psychiatric licensure in Taiwan. Chatbot responses were scored for correctness, and we used the Rasch model to evaluate chatbot ability. RESULTS: < 0.001), with a 1.92 logits improvement compared to the passing threshold. It demonstrated strengths in complex psychiatric problems and ethical understanding, yet it presented limitations in up-to-date legal updates and specialized psychiatry knowledge, such as recent amendments to the Mental Health Act, psychopharmacology, and advanced neuroimaging. CONCLUSIONS: Chatbot technology could be a valuable tool for medical education and clinical decision support in psychiatry, and as technology continues to advance, these models are likely to play an increasingly integral role in psychiatric practice.

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