用少量数据自动生成皮肤癌诊疗结构化病历，减轻医生负担。
Skin-SOAP: A Weakly Supervised Framework for Generating Structured SOAP Notes
- 弱监督多模态框架，从病变图像和简短文本生成病历。
- 性能接近GPT-4o等大模型，在临床相关性上表现优异。
- 专设医学概念评估与临床连贯性评分，更贴合真实医疗需求。
皮肤癌是全球最常见癌症，年医疗支出超80亿美元。早期诊断与及时治疗对提升生存率至关重要。临床上，医生通过结构化SOAP（主诉、客观、评估、计划）笔记记录就诊情况，但手动撰写耗时费力，加剧医生倦怠。本文提出skin-SOAP，一种弱监督多模态框架，仅需病变图像和少量临床文本即可生成结构化SOAP笔记。该方法减少对手动标注的依赖，实现可扩展、临床可信的文档生成，缓解医生工作压力并降低对大规模标注数据的需求。实验表明，其在关键临床相关性指标上达到与GPT-4o、Claude及DeepSeek Janus Pro相当的水平。为评估临床相关性，我们引入两项新指标：MedConceptEval（衡量与专家医学概念的语义对齐）和Clinical Coherence Score（CCS，评估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. Early diagnosis, accurate and timely treatment are critical to improving patient survival rates. 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 skin-SOAP, 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 this clinical relevance, 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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