Skin-SOAP: A Weakly Supervised Framework for Generating Structured SOAP Notes
This addresses clinician burnout and scalability in medical documentation for skin cancer care, but it is incremental as it builds on existing multimodal and weakly supervised approaches.
The paper tackles the problem of generating structured SOAP notes for skin carcinoma diagnosis by proposing a weakly supervised multimodal framework that uses lesion images and sparse clinical text, achieving performance comparable to models like GPT-4o and introducing new clinical relevance metrics.
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.