Beyond the Individual: Virtualizing Multi-Disciplinary Reasoning for Clinical Intake via Collaborative Agents
2026-04-10 • Multiagent Systems
Multiagent Systems
AI summaryⓘ
The authors created Aegle, a system that helps doctors make better decisions during outpatient visits by connecting multiple expert agents in real time. Instead of one doctor doing everything quickly and possibly missing details, Aegle splits tasks among specialist agents who work together to collect evidence and reason about diagnoses. This teamwork approach mimics a multi-disciplinary team but works faster and is easier to use. Tests showed Aegle improved the quality of medical notes and accuracy of diagnoses compared to other models. The authors provide their code openly for others to use.
outpatient consultationcognitive biasmulti-disciplinary team (MDT)SOAP notemulti-agent systemsclinical decision supportdiagnostic reasoningclinical documentationmachine learninghealthcare AI
Authors
Huangwei Chen, Wu Li, Junhao Jia, Yining Chen, Xiaotao Pang, Ya-Long Chen, Li Gonghui, Haishuai Wang, Jiajun Bu, Lei Wu
Abstract
The initial outpatient consultation is critical for clinical decision-making, yet it is often conducted by a single physician under time pressure, making it prone to cognitive biases and incomplete evidence capture. Although the Multi-Disciplinary Team (MDT) reduces these risks, they are costly and difficult to scale to real-time intake. We propose Aegle, a synchronous virtual MDT framework that brings MDT-level reasoning to outpatient consultations via a graph-based multi-agent architecture. Aegle formalizes the consultation state using a structured SOAP representation, separating evidence collection from diagnostic reasoning to improve traceability and bias control. An orchestrator dynamically activates specialist agents, which perform decoupled parallel reasoning and are subsequently integrated by an aggregator into a coherent clinical note. Experiments on ClinicalBench and a real-world RAPID-IPN dataset across 24 departments and 53 metrics show that Aegle consistently outperforms state-of-the-art proprietary and open-source models in documentation quality and consultation capability, while also improving final diagnosis accuracy. Our code is available at https://github.com/HovChen/Aegle.