用AI自动生成肺癌会诊病例摘要，提升讨论效率。
Development, Evaluation, and Deployment of a Multi-Agent System for Thoracic Tumor Board
- 构建自动化的AI摘要系统，替代人工撰写病例摘要。
- 评估结果显示AI摘要与医生标准摘要相当，事实准确率高。
- 采用大模型作为评判工具，适合医疗AI落地研究者参考。
肿瘤会诊是多学科团队针对患者制定治疗建议的重要会议，需实时审阅影像和病理数据。为提升讨论效率，需生成简洁的患者病例摘要。我们开发了一种基于AI的手动工作流，在斯坦福胸腔肿瘤会诊中实时展示病例摘要。为进一步减少人工干预，我们提出了多种自动化AI图表摘要方法，并与医师标注的黄金标准摘要及基于事实的评分标准进行对比评估。报告了这些比较结果，以及最终自动化AI摘要工具的部署情况和部署后的持续监控。此外，验证了使用大语言模型作为事实性评分裁判的有效性。本研究展示了将AI工作流融入临床常规实践的可行性。
原文摘要 · Abstract (English)
Tumor boards are multidisciplinary conferences dedicated to producing actionable patient care recommendations with live review of primary radiology and pathology data. Succinct patient case summaries are needed to drive efficient and accurate case discussions. We developed a manual AI-based workflow to generate patient summaries to display live at the Stanford Thoracic Tumor board. To improve on this manually intensive process, we developed several automated AI chart summarization methods and evaluated them against physician gold standard summaries and fact-based scoring rubrics. We report these comparative evaluations as well as our deployment of the final state automated AI chart summarization tool along with post-deployment monitoring. We also validate the use of an LLM as a judge evaluation strategy for fact-based scoring. This work is an example of integrating AI-based workflows into routine clinical practice.
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