Large Language Model's Advanced Reasoning Capabilities in Detecting Contraindications in Medical Exams (Preprint)

Abstract

<sec> <title>BACKGROUND</title> In medical practice, improving clinical reasoning and reducing diagnostic errors are essential. OpenAI introduced "OpenAI-o1" with enhanced capabilities for complex reasoning; however, it remains uncertain whether OpenAI-o1 can decrease diagnostic errors compared to the current model, GPT-4. </sec> <sec> <title>OBJECTIVE</title> We hypothesize that OpenAI-o1, compared to GPT-4, demonstrates greater proficiency in avoiding contraindicated options during the Japanese National Medical Licensing Examination (JNMLE), where candidates must avoid selecting any contraindicated options to pass. </sec> <sec> <title>METHODS</title> This study utilized questions from the JNMLE ranging from 2019 to 2024, specifically selecting those that included contraindications as potential answers. We administered 15 text-based questions to both GPT-4 and Open AI-o1 as follows. Step 1: Each question was first submitted to either GPT-4 or Open AI-o1 in Japanese. The model was tasked with identifying the correct answer. Step 2: The same question was presented to the model in Japanese. The model was instructed to select the contraindication instead of the correct answer. Step 3: Steps 1 and 2 were repeated with the questions translated into English. </sec> <sec> <title>RESULTS</title> GPT-4 correctly answered 12 out of 15 questions (80%) and identified 11 contraindications (73%) in Japanese. In English, GPT-4 correctly answered 13 questions (87%) and identified 11 contraindications (73%). Conversely, Open AI-o1 correctly answered 15 questions (100%), and identified 13 contraindications in Japanese (87%). </sec> <sec> <title>CONCLUSIONS</title> Compared to GPT-4, OpenAI-o1 demonstrated superior accuracy and the selection of contraindicated options in the JNMLE, particularly when using English. Further research is needed to explore whether these models can contribute to reducing medical diagnostic errors. </sec>

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