Abstract
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.
Abstract (translated)
皮肤癌是全球最常见的癌症类型，每年的医疗保健支出超过80亿美元。在临床环境中，医生使用详细的SOAP（主诉、客观检查、评估和计划）笔记来记录患者的就诊情况。然而，手动生成这些笔记耗时且繁重，导致了医师的职业倦怠。在这项工作中，我们提出了一种弱监督多模态框架，可以从有限的输入数据中自动生成结构化的临床SOAP笔记，包括病变图像和稀疏的临床文本信息。我们的方法减少了对人工标注的依赖，实现了可扩展、基于临床实践的文档记录，并减轻了医生的工作负担以及减少大规模注释数据的需求。我们的方法在关键临床相关性指标上达到了与GPT-4o、Claude和DeepSeek Janus Pro相当的表现水平。为了评估临床质量，我们引入了两个新颖的评价指标：MedConceptEval 和临床连贯度评分（CCS），分别用于评估语义与专家医学概念以及输入特征的一致性。
URL
https://arxiv.org/abs/2506.10328
