用AI自动生成放射科病例记录，减轻培训负担。
Intelligent Documentation in Medical Education: Can AI Replace Manual Case Logging?
- 用提示工程让大模型从报告中提取结构化信息
- 最佳模型F1值达0.87，兼顾准确与效率
- 适合医学教育场景的AI辅助文档系统
介入放射学培训中的操作病例记录是核心要求，但手动撰写耗时且易不一致。本研究探讨大语言模型（LLMs）能否直接从自由文本放射科报告中自动化生成结构化病例记录。在9名住院医师2018至2024年间撰写的414份精选介入放射学报告上，评估了多个本地与商用LLM在指令式和思维链提示下的表现。通过敏感性、特异性与F1分数评估模型性能，并结合推理延迟与令牌效率分析运营成本。结果表明，本地与商用模型均实现良好提取效果，最佳F1值接近0.87，且在速度与成本间呈现不同权衡。利用LLM实现自动化可显著降低学员文书负担，提升记录一致性。研究证实了在医学教育中使用AI辅助文档的可行性，并强调需在更多机构与临床流程中进一步验证。
原文摘要 · Abstract (English)
Procedural case logs are a core requirement in radiology training, yet they are time-consuming to complete and prone to inconsistency when authored manually. This study investigates whether large language models (LLMs) can automate procedural case log documentation directly from free-text radiology reports. We evaluate multiple local and commercial LLMs under instruction-based and chain-of-thought prompting to extract structured procedural information from 414 curated interventional radiology reports authored by nine residents between 2018 and 2024. Model performance is assessed using sensitivity, specificity, and F1-score, alongside inference latency and token efficiency to estimate operational cost. Results show that both local and commercial models achieve strong extraction performance, with best F1-scores approaching 0.87, while exhibiting different trade-offs between speed and cost. Automation using LLMs has the potential to substantially reduce clerical burden for trainees and improve consistency in case logging. These findings demonstrate the feasibility of AI-assisted documentation in medical education and highlight the need for further validation across institutions and clinical workflows.
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