多个大型语言模型与经验丰富的医生在诊断胃肠道症状具有挑战性的病例时
Xintian Yang1, Tongxin Li1, Han Wang2
1State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, China.
NPJ digital medicine
|February 5, 2025
概括
大型语言模型 (LLM) 在帮助医生进行具有挑战性的诊断方面表现有前途. 克劳德 3.5 索内特在诊断复杂的胃肠道病例方面表现优于人类胃肠科医生.
科学领域:
- 医学诊断 医学诊断 医学诊断
- 医疗保健中的人工智能
- 胃肠病学 胃肠病学
背景情况:
- 医生越来越多地咨询大型语言模型 (LLM) 来解决复杂的诊断挑战.
- 与人类专家相比,LLM的诊断能力仍然是积极研究的领域.
研究的目的:
- 为了比较LLM的诊断准确度与人类胃肠科医生对于具有挑战性的胃肠道病例.
- 评估LLM作为临床实践中的诊断辅助工具的有效性.
主要方法:
- 使用了67个具有挑战性的胃肠道病例的离线数据集.
- 七名LLM和22名胃肠科医生负责为这些病例提供诊断.
- 在LLM和人类医生之间进行了诊断覆盖和教学诊断的比较.
主要成果:
- 克劳德3.5索内特提供了最高比例的教导性诊断 (76.1%),明显超过了所有参与的胃肠科医生.
- 克劳德3.5索内特的诊断覆盖率 (76.1%) 显著高于使用传统资源的胃肠科医生 (45.5%).
- 性能差异具有统计学意义 (胃肠病学家比较p < 0.05,资源比较p < 0.001).
结论:
- 先进的LLM,如克劳德3.5索内特,与人类专家相比,在挑战性胃肠道病例方面表现出卓越的诊断能力.
- 在复杂的诊断场景中,LLM可以作为有价值,节省时间和具有成本效益的工具来帮助胃肠科医生.
- 这些发现表明,人工智能在提高专业医疗领域的诊断准确性和效率方面可能发挥作用.
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