基于推理的LLM在医学社会技能方面超过了人类平均表现
Khalid Ibraheem Alohali1, Laura Asaad Almusaeeb2, Abdulaziz Abdulrahman Almubarak2
1College of Medicine, King Saud University, Riyadh, 11461, Saudi Arabia. khalid.i.alohali@gmail.com.
Scientific reports
|October 17, 2025
概括
基于推理的AI模型,如o1,在医学社会技能问题上表现出色,在许可证考试中表现优于其他大型语言模型 (LLM). 这些先进的人工智能工具显示出改善医疗教育和患者护理的前景.
科学领域:
- 医疗保健中的人工智能
- 医疗教育 技术 技术 医学教育
- 自然语言处理自然语言处理.
背景情况:
- 医疗执照考试的重点是社会技能 (沟通,道德,专业精神),这对于患者的护理至关重要.
- 人工智能在医疗保健中的整合引发了人们对其处理以人为中心的场景的能力的质疑.
- 之前的研究表明,大型语言模型 (LLM) 在USMLE社会技能问题上表现良好.
研究的目的:
- 评估五个LLM (GPT-4,GPT-4o,Gemini 1.5 Pro,o1-预览,o1) 在USMLE样式的社会技能问题上的表现.
- 评估推理能力 (思维链) 对社会技能场景中的LLM绩效的影响.
- 在怀疑的后续提示下测试LLM的一致性.
主要方法:
- 利用了来自UWORLD问题库的40个USMLE式社交技能问题.
- 涵盖的领域包括沟通,医疗保健政策,基于系统的实践和医疗伦理.
- 进行了"你确定吗?"的测试. 立即后回复以评估一致性.
主要成果:
- 基于推理的模型o1获得了最高的准确性 (97.5%),正确回答了40个问题中的39个.
- GPT-4o和Gemini 1.5 Pro同步排名第二 (87.5%),表现优于GPT-4 (75%) 和o1-预览版 (77.5%).
- 所有评估的LLM都超过了UWORLD问题库平均64%.
结论:
- 基于推理的LLM,特别是o1,在复杂的,以社会为导向的医疗任务中表现出卓越的巨大潜力.
- GPT-4o和Gemini 1.5 Pro显示出不同的领域优势,突出显示了定制AI应用程序的需要.
- 推理模型的持续高性能表明它们在补充临床培训,医学教育和患者护理方面的价值.
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