Abstract
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.
Abstract (translated)
皮肤癌是全球最常见的癌症形式，每年的医疗保健支出超过80亿美元。早期诊断、准确及时的治疗对于提高患者生存率至关重要。在临床环境中，医生通过详细的SOAP（主诉、客观检查、评估和计划）记录来记录患者的就诊情况。然而，手动生成这些记录既费时又增加了医务人员的工作负担，导致职业倦怠。为此，我们提出了skin-SOAP，这是一种弱监督多模态框架，可以从有限的输入（包括病变图像和稀疏的临床文本）生成结构化的SOAP记录。我们的方法减少了对人工注释的依赖，使文档制作更具规模性和临床实用性，同时减轻了医务人员的工作负担，并减少了对大规模标注数据的需求。我们提出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相当的性能水平。为了评估其临床相关性，我们引入了两个新的评价指标：MedConceptEval 和临床连贯性评分（CCS），分别用于评估语义与专家医学概念及输入特征的一致性。
URL
https://arxiv.org/abs/2508.05019
