培训用于检测放射学报告中的错误的生成大型语言模型
Cong Sun1, Kurt Teichman2, Yiliang Zhou1
1Department of Population Health Sciences, Weill Cornell Medicine, 575 Lexington Ave, New York, NY 10022.
Radiology
|May 20, 2025
概括
大型语言模型 (LLM) 通过检测放射学报告中的错误,显著改善了医学校对. 精心调整的LLM,如Llama-3,在识别否定,左/右,间隔和转录错误方面表现出高准确性.
科学领域:
- 人工智能在医学中的应用
- 医疗信息学 医疗信息学
- 放射学报告 放射学报告
背景情况:
- 大型语言模型 (LLM) 显示了医学校对的潜力,但它们在放射学报告错误检测中的使用是有限的.
- 开发用于准确分析放射学报告的AI工具对于患者安全和临床决策至关重要.
研究的目的:
- 开发和评估用于检测放射学报告中的各种错误的生成LLM.
- 评估不同LLM的表现,并在医学校对任务中提示策略.
主要方法:
- 创建了一个合成和现实世界放射学报告 (MIMIC-CXR) 的数据集,包括无错误和错误的报告.
- 错误被分为否定,左/右,间隔变化和转录类型.
- 包括Llama-3,GPT-4和BiomedBERT在内的模型使用零射击,少数射击和微调策略进行了微调,性能通过F1分数和放射科医生审查进行评估.
主要成果:
- 精心调整的Llama-3-70B-Instruct模型获得了0.780的F1总体最高分,转录错误 (0.828) 的特定高分.
- 放射科医生审查证实了该模型检测错误的能力,其中99份报告由两位审查员证实,163份报告由至少一名审查员证实.
结论:
- 生成型的LLM,当对各种放射学报告数据集进行微调时,会大大提高医学校对的准确性.
- 这些人工智能模型为改善放射学报告的质量和可靠性提供了有希望的解决方案.
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