临床途径意识到可靠和透明的医疗对话的大型语言模型
Jiageng Wu1, Xian Wu2, Yefeng Zheng3
1Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Journal of biomedical informatics
|November 2, 2025
概括
这项研究介绍了MedKP,一个医学对话系统,使用临床推理来改善远程医疗的大型语言模型 (LLM) 响应. MedKP提高了可靠性,减少了幻觉,使人工智能在医疗保健中更值得信赖.
科学领域:
- 医疗保健中的人工智能
- 用于医疗应用的自然语言处理.
- 临床决策支持系统 临床决策支持系统
背景情况:
- 大型语言模型 (LLM) 显示了实时医疗查询的潜力,但遭受了漫长,通用和幻觉输出.
- 现有的AI系统缺乏强大的临床推理和透明度,限制了它们在医疗保健环境中的可信度.
研究的目的:
- 开发一个可靠和可解释的医疗对话系统 (MedKP),结合临床推理.
- 为了减轻幻觉和提高AI产生的医疗反应的准确性.
- 改善人工智能的整合到临床工作流程中,以获得更好的远程医疗服务.
主要方法:
- 开发了MedKP,集成医学知识图和临床途径编码来模拟医生推理.
- 在MedDG和KaMed数据集上评估MedKP,将性能与GPT-4o和LLaMA3.1-70B.等最先进的模型进行比较.
- 采用多维评估框架,包括临床相关性,文本相似性,语义对齐以及通过LLM和人类评估检测幻觉.
主要成果:
- 在两种数据集中,MedKP (6B) 的表现优于先进基线,显示出卓越的临床相关性和与人类医生的响应对齐.
- 在医疗实体认可F1分数方面取得了显著改善:MedDG的31.41%和KaMed的26.62%.
- 废弃性研究证实了MedKP模型中的单个成分的有效性.
结论:
- 临床推理对于在数字医疗保健中推进可信的人工智能至关重要.
- 途径意识的MedKP方法提高了AI响应的可靠性,连贯性和透明度.
- 该系统弥合了LLM和临床实践之间的差距,提高了远程医疗的可访问性和质量.
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