# Towards Scalable SOAP Note Generation: A Weakly Supervised Multimodal Framework

arXiv: https://arxiv.org/abs/2506.10328 · HTML: https://mlsanity.com/paper/2506.10328/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 31 references that are in the map.
- First posted on arXiv 12 Jun 2025.

## Builds on (most cited first, 15 of 15)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
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- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [PAD-UFES-20: a skin lesion dataset composed of patient data and clinical images collected from smartphones](https://mlsanity.com/paper/2007.00478/) (2020)
- [CapArena: Benchmarking and Analyzing Detailed Image Captioning in the LLM Era](https://mlsanity.com/paper/2503.12329/) (2025)
- [Intelligent Clinical Documentation: Harnessing Generative AI for Patient-Centric Clinical Note Generation](https://mlsanity.com/paper/2405.18346/) (2024)
- [Large Language Model Benchmarks in Medical Tasks](https://mlsanity.com/paper/2410.21348/) (2024)
- [Efficient Fine-Tuning of Large Language Models for Automated Medical Documentation](https://mlsanity.com/paper/2409.09324/) (2024)
- [Enhancing Clinical Efficiency through LLM: Discharge Note Generation for Cardiac Patients](https://mlsanity.com/paper/2404.05144/) (2024)
- [Towards an Automated SOAP Note: Classifying Utterances from Medical Conversations](https://mlsanity.com/paper/2007.08749/) (2020)
- [Exploring Robustness in Doctor-Patient Conversation Summarization: An Analysis of Out-of-Domain SOAP Notes](https://mlsanity.com/paper/2406.02826/) (2024)
- [Assessment of AI-Generated Pediatric Rehabilitation SOAP-Note Quality](https://mlsanity.com/paper/2503.15526/) (2025)

## Abstract (arXiv)

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.