MedGellan：由大语言模型生成的医疗指导以支持医生
MedGellan: LLM-Generated Medical Guidance to Support Physicians
AI总结：
MedGellan提出一种轻量级、无需标注的LLM框架，通过贝叶斯启发式提示从医疗记录生成临床指导，辅助医生诊断，实验表明可提升召回率和F1分数。
摘要：
医疗决策是一项关键任务，其中的错误可能导致严重甚至危及生命的后果。虽然完全自动化仍具挑战性，但将机器智能与人类监督相结合的混合框架提供了一种实用的替代方案。在本文中，我们提出了MedGellan，一个轻量级、无需标注的框架，它使用大语言模型（LLM）从原始医疗记录中生成临床指导，随后由医生利用该指导来预测诊断。MedGellan采用了一种受贝叶斯启发的提示策略，该策略尊重临床数据的时间顺序。初步实验表明，使用MedGellan的LLM生成的指导提高了诊断性能，特别是在召回率和$F_1$分数方面。
英文摘要：
Medical decision-making is a critical task, where errors can result in serious, potentially life-threatening consequences. While full automation remains challenging, hybrid frameworks that combine machine intelligence with human oversight offer a practical alternative. In this paper, we present MedGellan, a lightweight, annotation-free framework that uses a Large Language Model (LLM) to generate clinical guidance from raw medical records, which is then used by a physician to predict diagnoses. MedGellan uses a Bayesian-inspired prompting strategy that respects the temporal order of clinical data. Preliminary experiments show that the guidance generated by the LLM with MedGellan improves diagnostic performance, particularly in recall and $F_1$ score.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
