复杂的临床文档中人工智能辅助的错误检测:利用大型语言模型提高瘤学患者安全
Peter May1, Sina Nokodian1, Christoph Nuernbergk1
1Department of Medicine III, School of Medicine and Health, Technical University of Munich, Munich, Germany.
JCO clinical cancer informatics
|January 6, 2026
概括
边境大型语言模型 (LLM) 在检测复杂的瘤学临床文档中的错误方面明显优于人类专家. 这些人工智能工具有望提高医疗保健中的准确性和患者安全性.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 改善临床文档 改善临床文档
背景情况:
- 瘤学等高风险专业的临床文档错误可能会导致严重的患者伤害.
- 需要先进的安全检查,以确保复杂的医疗记录的准确性.
研究的目的:
- 评估边界大型语言模型 (LLM) 在复杂的瘤临床文档中识别和纠正错误的能力.
- 在检测文档错误方面,将LLM绩效与人类专家进行比较.
主要方法:
- 在血液学/瘤学中使用合成临床简报和出院摘要进行了两阶段的评估.
- 评估了LLM (GPT-4-mini,Gemini 2.5 Pro,Gemma 3 27B) 与人类临床医生进行错误检测和定位.
- 与人类专家数据对比LLM的性能,以获得准确性和速度.
主要成果:
- 在错误检测和定位任务中,LLM,特别是Gemini 2.5 Pro,显著超过了人类专家.
- 双子 2.5 Pro 实现了高精度 (0.928 标记, 0.915 定位) 并确定了 97.8% 的错误排放总结.
- 先进的LLM表现出卓越的速度和准确性,具有人与人工智能的协同协作潜力.
结论:
- 与人类专家和本地模型相比,前沿的LLM提供了卓越的错误检测能力和效率.
- LLM可以作为强大的助手来减少临床医生的工作量和文档错误.
- 将LLM驱动的错误标记集成到EHR中可以提高瘤学文档的准确性,治疗质量和患者安全.
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