# Automated Clinical Problem Detection from SOAP Notes using a Collaborative Multi-Agent LLM Architecture

arXiv: https://arxiv.org/abs/2508.21803 · HTML: https://mlsanity.com/paper/2508.21803/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 7 of its 11 references that are in the map.
- First posted on arXiv 29 Aug 2025.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 7 of 7)

- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [Mixture-of-Agents Enhances Large Language Model Capabilities](https://mlsanity.com/paper/2406.04692/) (2024)
- [Multi-Agent Collaboration Mechanisms: A Survey of LLMs](https://mlsanity.com/paper/2501.06322/) (2025)
- [Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents](https://mlsanity.com/paper/2306.03314/) (2023)
- [Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents](https://mlsanity.com/paper/2405.02957/) (2024)
- [Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understanding](https://mlsanity.com/paper/2204.03035/) (2022)

## Built on by (most cited first, 4 of 4)

- [Do Mixed-Vendor Multi-Agent LLMs Improve Clinical Diagnosis?](https://mlsanity.com/paper/2603.04421/) (2026)
- [A Multi-Agent Framework for Interpreting Multivariate Physiological Time Series](https://mlsanity.com/paper/2603.04142/) (2026)
- [MediHive: A Decentralized Agent Collective for Medical Reasoning](https://mlsanity.com/paper/2603.27150/) (2026)
- [VetClaw: An Edge-Cloud Multimodal Agentic System for Veterinary Disease Screening](https://mlsanity.com/paper/2607.26042/) (2026)

## Abstract (arXiv)

Accurate interpretation of clinical narratives is critical for patient care, but the complexity of these notes makes automation challenging. While Large Language Models (LLMs) show promise, single-model approaches can lack the robustness required for high-stakes clinical tasks. We introduce a collaborative multi-agent system (MAS) that models a clinical consultation team to address this gap. The system is tasked with identifying clinical problems by analyzing only the Subjective (S) and Objective (O) sections of SOAP notes, simulating the diagnostic reasoning process of synthesizing raw data into an assessment. A Manager agent orchestrates a dynamically assigned team of specialist agents who engage in a hierarchical, iterative debate to reach a consensus. We evaluated our MAS against a single-agent baseline on a curated dataset of 420 MIMIC-III notes. The dynamic multi-agent configuration demonstrated consistently improved performance in identifying congestive heart failure, acute kidney injury, and sepsis. Qualitative analysis of the agent debates reveals that this structure effectively surfaces and weighs conflicting evidence, though it can occasionally be susceptible to groupthink. By modeling a clinical team's reasoning process, our system offers a promising path toward more accurate, robust, and interpretable clinical decision support tools.