用少量数据自动生成专业皮肤癌就诊记录，减轻医生负担。
Towards Scalable SOAP Note Generation: A Weakly Supervised Multimodal Framework
- 弱监督多模态框架，仅需病变图像和简略文本生成结构化病历
- 性能媲美GPT-4o等大模型，在临床相关性指标上表现优异
- 提出新评估指标，更准确衡量医疗概念对齐与内容连贯性
皮肤癌是全球最常见的癌症，每年医疗支出超80亿美元。临床中，医生通过详细的SOAP（主观、客观、评估、计划）笔记记录就诊情况，但手动撰写耗时费力，加剧医生职业倦怠。本文提出一种弱监督多模态框架，仅需有限输入——包括病变图像和稀疏临床文本——即可生成结构化且符合临床规范的SOAP笔记。该方法大幅降低对人工标注的依赖，实现可扩展的临床文档生成，缓解医生负担并减少大规模标注数据需求。实验表明，该方法在关键临床相关性指标上表现与GPT-4o、Claude及DeepSeek Janus Pro相当。为评估临床质量，我们引入两个新指标：MedConceptEval（评估与专家医学概念的语义对齐）和Clinical Coherence Score（CCS，评估内容与输入特征的一致性）。
原文摘要 · Abstract (English)
Skin carcinoma is the most prevalent form of cancer globally, accounting for over $8 billion in annual healthcare expenditures. In clinical settings, physicians document patient visits using detailed SOAP (Subjective, Objective, Assessment, and Plan) notes. However, manually generating these notes is labor-intensive and contributes to clinician burnout. In this work, we propose a weakly supervised multimodal framework to generate clinically structured SOAP notes from limited inputs, including lesion images and sparse clinical text. Our approach reduces reliance on manual annotations, enabling scalable, clinically grounded documentation while alleviating clinician burden and reducing the need for large annotated data. Our method achieves performance comparable to GPT-4o, Claude, and DeepSeek Janus Pro across key clinical relevance metrics. To evaluate clinical quality, we introduce two novel metrics MedConceptEval and Clinical Coherence Score (CCS) which assess semantic alignment with expert medical concepts and input features, respectively.
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