在领先的大型语言模型 (LLM) 中,代理人工智能和医生的临床病史的比较表现
medRxiv : the preprint server for health sciences
|February 6, 2026
概括
大型语言模型 (LLM) 通过使用代理框架有效地收集患者病史. 这种人工智能方法确保了准确的临床数据收集,提高了医疗保健质量.
科学领域:
- 人工智能在医学中的应用
- 临床信息学 临床信息学
- 自然语言处理自然语言处理.
背景情况:
- 综合性临床病史记录对于准确的诊断和有效的患者护理至关重要.
- 目前的历史收集方法可能耗时,可能错过关键细节.
- 集成先进的人工智能,特别是大型语言模型 (LLM),为优化这一过程提供了潜力.
研究的目的:
- 评估使用LLMs进行有效和全面的临床病史记录的药物治疗框架的有效性.
- 通过开发的LLM系统来评估患者病史的准确性和完整性.
- 确定LLM驱动工具在生成临床摘要,差异诊断和调查建议方面的潜力.
主要方法:
- 在结构化的代理框架内开发使用LLM (GPT-4o,Gemini-2.5-Flash-Lite,Grok-3) 的代提示系统.
- 评估使用52个已发表的病例报告和20个模拟的患者互动.
- 盲人医生对被捕获的病史元素的相关性和完整性的评估.
- 生成EHR准备的摘要,差异诊断,以及互动后推的调查.
主要成果:
- 在所有测试模型中,LLM框架在捕获相关历史元素方面实现了>85%的准确性和F1分数.
- 医生评估证实了收集的临床病史的高质量和完整性.
- 系统产生的推调查与用于最终诊断的调查保持一致.
结论:
- 代理的LLM系统显示了结构化和高效的临床病史收集的巨大潜力.
- 开发的框架可以准确地提取临床上有意义的信息,有助于诊断和护理计划.
- 进一步的前性临床评估是有必要的,以验证这些发现在现实世界的医疗保健环境.
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