Towards Scalable SOAP Note Generation: A Weakly Supervised Multimodal Framework
This addresses clinician burnout and healthcare costs by automating documentation, but it is incremental as it builds on existing multimodal and weakly supervised methods.
The paper tackles the problem of generating structured SOAP notes for skin carcinoma from limited inputs like lesion images and sparse text, achieving performance comparable to models like GPT-4o and introducing new metrics for clinical quality evaluation.
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.