大型语言模型 视网膜分离 患者紧急电话呼叫:试点研究
Rohini Chahal1, Flavius Beca2, Viet Q Chau3
1McGovern Medical School at UTHealth Houston, Houston, TX, USA.
Journal of vitreoretinal diseases
|January 28, 2026
概括
人类专家在通过电话诊断和管理紧急视网膜病例方面表现优于人工智能聊天机器人. 然而,像ChatGPT和Claude这样的特定的大型语言模型 (LLM) 显示了与人类相似的准确性,这表明它们有可能作为诊断辅助工具.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 值班电话分拣对于管理紧急的眼科疾病至关重要.
- 大型语言模型 (LLM) 越来越多地被用于医学应用.
- 在实时临床场景中评估AI诊断准确度至关重要.
研究的目的:
- 为了比较LLM聊天机器人的诊断和管理准确性,与在电话紧急情况下的门诊视网膜分拣中的人类专家进行比较.
- 评估不同LLM聊天机器人的性能变化.
主要方法:
- 十个模拟的视网膜紧急情况被介绍给四个LLM聊天机器人,三个视网膜外科研究员和三个认证的眼科医生.
- 诊断和选建议被人类评估者收集并对准确性进行分级.
- 进行了统计分析,以比较聊天机器人和人类的性能.
主要成果:
- 与总体聊天机器人相比,人类专家在诊断 (95%) 和后续建议 (85%) 中的准确性明显更高 (分别为76.7%和70%).
- 与人类相比,ChatGPT和Claude的诊断 (90%和83.3%) 和推 (93.3%和90%) 准确度均不低于人类.
- 双子座和Meta聊天机器人在诊断和管理任务中表现明显比人类差.
结论:
- 虽然人类专家目前在视网膜紧急分拣方面表现优于LLM聊天机器人,但ChatGPT和Claude等特定模型显示了可比的准确性.
- 在临床应用广泛之前,LLM的可变性能凸显了进一步验证的必要性.
- 某些LLM在未来可以作为管理紧急医疗电话的有价值的辅助工具.
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