评估AI在医学知识测试中的有效性:一项使用2020年至2023年台湾内部医学考试问题的研究
Shih-Yi Lin1,2, Ying-Yu Hsu3, Shu-Woei Ju2
1Graduate Institute of Biomedical Sciences, College of Medicine, China Medical University, Taichung.
Digital health
|October 21, 2024
概括
生成型人工智能 (AI) 模型在医学检查中表现不同. 克劳德3.5索内特在专业医疗知识和解决问题的评估中总体上表现优于GPT-4o和双子星高级.
科学领域:
- 人工智能在医学中的应用
- 医疗教育 技术 技术 医学教育
- 临床决策支持系统 临床决策支持系统
背景情况:
- 生成型人工智能 (AI) 越来越多地在专业领域探索其潜力.
- 评估人工智能处理复杂医学知识的能力对于其融入医疗保健和教育至关重要.
研究的目的:
- 在正式的内科医学检查中评估领先的生成性AI模型的性能.
- 在医疗环境中比较GPT-4o,克劳德3.5索内特和双子座高级的解决问题能力.
主要方法:
- 从2020年到2023年使用的内部医学考试问题.
- 测试了GPT-4o,克劳德3.5索内特和双子座高级模型.
- 使用IBM SPSS统计26分析性能,计算准确度并执行统计测试 (皮尔森相关性,ANOVA).
主要成果:
- 克劳德3.5索内特获得了最高的平均得分 (84.85%),超过了GPT-4o (81.97%) 和双子星高级 (69.84%).
- 克劳德3.5索内特在精神病学和病学方面表现出色;GPT-4o在血液学,瘤学和病学方面表现出色.
- 所有模型在基于文本而不是基于图像的问题上表现得更好,Claude 3 Opus在COVID-19相关问题上表现强.
结论:
- 生成型人工智能模型在医学知识和问题解决方面表现出不同水平的熟练程度.
- 克劳德3.5索内特表现出优越和一致的表现,表明AI在医疗教育支持方面的巨大潜力.
- 需要进一步的研究,以了解不同医学专业和问题格式的AI性能变化.
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