大型语言模型在医学检查中检测禁忌选项的高级推理能力。

IF 3.1 3区医学 Q2 MEDICAL INFORMATICS

JMIR Medical Informatics Pub Date : 2025-05-12 DOI:10.2196/68527

Yuichiro Yano, Mizuki Ohashi, Taiju Miyagami, Hirotake Mori, Yuji Nishizaki, Hiroyuki Daida, Toshio Naito

引用次数: 0

摘要

未标记：加强临床推理和减少诊断错误在医疗实践中至关重要；openai - 01具有先进的推理能力，在日本国家医疗执照考试的15个问题上表现优于GPT-4(准确率：100% vs 80%；禁忌症选项检测：87% vs 73%)，尽管由于样本量小，结果是初步的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

The Advanced Reasoning Capabilities of Large Language Models for Detecting Contraindicated Options in Medical Exams.

Unlabelled: Enhancing clinical reasoning and reducing diagnostic errors are essential in medical practice; OpenAI-o1, with advanced reasoning capabilities, performed better than GPT-4 on 15 Japanese National Medical Licensing Examination questions (accuracy: 100% vs 80%; contraindicated option detection: 87% vs 73%), though findings are preliminary due to the small sample size.

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来源期刊

JMIR Medical Informatics Medicine-Health Informatics

CiteScore

7.90

自引率

3.10%

发文量

173

审稿时长

12 weeks

期刊介绍： JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals. Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2016: 5.175), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.