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arxiv: 2604.08927 · v3 · submitted 2026-04-10 · 💻 cs.MA

Recognition: unknown

Beyond the Individual: Virtualizing Multi-Disciplinary Reasoning for Clinical Intake via Collaborative Agents

Huangwei Chen , Wu Li , Junhao Jia , Yining Chen , Xiaotao Pang , YaLong Chen , Gonghui Li , Haishuai Wang
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Jiajun Bu Lei Wu
Authors on Pith no claims yet

Pith reviewed 2026-05-10 17:26 UTC · model grok-4.3

classification 💻 cs.MA
keywords multi-agent systemsvirtual MDTclinical intakeSOAP notescollaborative reasoningbias controldiagnosis support
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The pith

Aegle virtualizes multi-disciplinary team reasoning with graph-based agents and SOAP separation to improve outpatient clinical intake.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper addresses the problem of single-physician outpatient consultations, which are prone to cognitive biases and incomplete evidence under time pressure. It proposes Aegle as a synchronous virtual MDT that uses a graph-based multi-agent architecture to simulate expert collaboration in real time. The system structures consultations with SOAP notes to separate evidence collection from diagnostic reasoning, enabling decoupled parallel work by specialist agents that an orchestrator activates and an aggregator integrates. Experiments across benchmarks and real-world data from 24 departments demonstrate gains in documentation quality, consultation capability, and diagnosis accuracy over existing models.

Core claim

Aegle is a synchronous virtual MDT framework that brings MDT-level reasoning to outpatient consultations via a graph-based multi-agent architecture. It 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.

What carries the argument

Graph-based multi-agent architecture with an orchestrator for dynamic specialist activation, decoupled parallel reasoning by agents, and an aggregator for integration, all operating on separated SOAP evidence and diagnosis states.

Load-bearing premise

The multi-agent graph architecture with parallel reasoning and SOAP separation actually delivers MDT-level bias control and traceability that improves real-world clinical decisions beyond benchmark scores.

What would settle it

A randomized trial showing no reduction in diagnostic errors or incomplete documentation when Aegle assists intake compared to standard single-physician practice.

Figures

Figures reproduced from arXiv: 2604.08927 by Gonghui Li, Haishuai Wang, Huangwei Chen, Jiajun Bu, Junhao Jia, Lei Wu, Wu Li, Xiaotao Pang, YaLong Chen, Yining Chen.

Figure 1
Figure 1. Figure 1: Single-view vs. virtual MDT reasoning for [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the Aegle framework. (a) A two-stage consultation workflow consisting of iterative history [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Stage I of Aegle, iterative history taking, illustrated with a pediatric heart murmur case. Patient responses [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Ablation study results on ClinicalBench and RAPID-IPN. (a) Performance Degradation: The score drop [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Correlation between LLM-as-a-judge scores and physician ratings on ClinicalBench and RAPID-IPN. [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Department-wise documentation quality comparison on ClinicalBench. IDEA (left) and SOAP (right) [PITH_FULL_IMAGE:figures/full_fig_p025_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The IPN of a 73-year-old male patient suffering from prostate cancer. [PITH_FULL_IMAGE:figures/full_fig_p026_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: The IPN of a 73-year-old male patient suffering from prostate cancer (continued). [PITH_FULL_IMAGE:figures/full_fig_p027_8.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces Aegle, a graph-based multi-agent framework that virtualizes a Multi-Disciplinary Team (MDT) for initial outpatient clinical intake. It uses a synchronous orchestrator to activate specialist agents performing decoupled parallel reasoning on a structured SOAP consultation state, followed by aggregator integration into a coherent clinical note. The central claim is that this architecture delivers MDT-level bias control and traceability, leading to consistent outperformance over proprietary and open-source models in documentation quality, consultation capability, and final diagnosis accuracy, as shown on ClinicalBench and the real-world RAPID-IPN dataset spanning 24 departments and 53 metrics. Code is released.

Significance. If the architecture's mechanisms for parallel reasoning and SOAP separation can be shown to causally improve bias mitigation and traceability beyond what simple ensembling or extended context provide, the work would offer a scalable alternative to resource-intensive human MDTs in routine care. The multi-dataset evaluation and open code strengthen potential impact in clinical AI and multi-agent systems.

major comments (2)
  1. [Experiments] Experiments section (and abstract): the claim of consistent outperformance across 53 metrics on ClinicalBench and RAPID-IPN is presented without reported statistical tests, error bars, baseline implementation details, or data exclusion rules. This leaves open whether gains derive from the graph orchestrator, decoupled agents, and SOAP separation or from unaccounted factors such as ensembling or context length.
  2. [Evaluation] Evaluation and method sections: no bias-injection experiments, clinician-rated bias scores, or quantitative traceability audits (e.g., source attribution completeness or reasoning path coverage) are described to isolate the contribution of parallel specialist reasoning and aggregator integration to reduced cognitive bias. The central MDT-level bias-control claim therefore rests on indirect benchmark gains rather than targeted validation.
minor comments (2)
  1. [Introduction] The abstract and introduction use 'synchronous' and 'decoupled parallel' without clarifying how true simultaneity is achieved in the graph execution model.
  2. [Figures/Tables] Figure captions and table legends should explicitly state the number of runs or seeds used for the reported metrics.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the detailed review and constructive feedback on our manuscript. We appreciate the opportunity to clarify and strengthen our presentation of Aegle's contributions. Below, we provide point-by-point responses to the major comments.

read point-by-point responses

  1. Referee: [Experiments] Experiments section (and abstract): the claim of consistent outperformance across 53 metrics on ClinicalBench and RAPID-IPN is presented without reported statistical tests, error bars, baseline implementation details, or data exclusion rules. This leaves open whether gains derive from the graph orchestrator, decoupled agents, and SOAP separation or from unaccounted factors such as ensembling or context length.

    Authors: We fully agree that reporting statistical significance, error bars, and detailed baseline information is essential for robust claims. In the revised manuscript, we will add statistical tests (such as paired t-tests or non-parametric equivalents with p-values adjusted for multiple comparisons) across the key metrics, include error bars in figures and tables, and expand the experimental setup section with precise descriptions of baseline model implementations, prompting strategies, and any data exclusion criteria used in the RAPID-IPN dataset. Furthermore, to directly address whether the gains stem from the proposed architecture rather than ensembling or context length, we will include additional ablation experiments comparing Aegle against (1) an ensemble of the same specialist models without the orchestrator and aggregator, and (2) single-model baselines with context lengths matched to Aegle's aggregated state. These revisions will help isolate the causal contributions of the graph-based parallel reasoning and SOAP separation. revision: yes

  2. Referee: [Evaluation] Evaluation and method sections: no bias-injection experiments, clinician-rated bias scores, or quantitative traceability audits (e.g., source attribution completeness or reasoning path coverage) are described to isolate the contribution of parallel specialist reasoning and aggregator integration to reduced cognitive bias. The central MDT-level bias-control claim therefore rests on indirect benchmark gains rather than targeted validation.

    Authors: We acknowledge that the current evaluation does not include direct bias-injection experiments or clinician-rated bias assessments, which would provide stronger causal evidence for the bias mitigation properties. The manuscript's claims are supported by consistent outperformance on real-world clinical data across multiple departments and metrics, which we interpret as evidence of improved bias control through the decoupled reasoning process. In the revision, we will add a new subsection in the discussion that explicitly discusses the mechanisms by which the orchestrator, specialist agents, and aggregator promote traceability (e.g., by maintaining separate SOAP components and logging agent contributions), and we will include quantitative traceability metrics such as reasoning path coverage where feasible from existing logs. We will also note the absence of bias-injection studies as a limitation and propose it as future work. This addresses the concern without requiring entirely new data collection at this stage. revision: partial

Circularity Check

0 steps flagged

No significant circularity: claims rest on external benchmarks and datasets

full rationale

The paper presents an empirical multi-agent system evaluated on independent external resources (ClinicalBench benchmark and real-world RAPID-IPN dataset spanning 24 departments and 53 metrics). No mathematical derivations, parameter fittings, or predictions are described that reduce by construction to the model's own inputs or self-citations. Performance comparisons are made against external proprietary and open-source baselines on held-out data, with no self-definitional loops, fitted-input predictions, or load-bearing self-citation chains. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the domain assumption that multi-agent collaboration can replicate human MDT benefits in bias reduction and evidence capture, plus the practical assumption that benchmark outperformance will generalize to clinical use.

axioms (1)
  • domain assumption Multi-disciplinary team reasoning reduces cognitive biases and improves evidence capture compared to single-physician intake.
    This premise is stated directly in the abstract as motivation for the virtual MDT approach.
invented entities (1)
  • Aegle framework no independent evidence
    purpose: Synchronous virtual MDT using graph-based multi-agent architecture
    The complete system including orchestrator, specialist agents, and aggregator is introduced by the paper.

pith-pipeline@v0.9.0 · 5511 in / 1265 out tokens · 56111 ms · 2026-05-10T17:26:19.883715+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

44 extracted references · 2 canonical work pages

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    **Based on the information**: All of your responses must be strictly based on the case text above

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    **No fabrication**: Do not imagine, extend, or invent any medical history or details that are not provided in the case. If the doctor asks about information that is completely absent from the materials, respond naturally with phrases such as “I’m not sure,” “I didn’t notice,” or “I don’t have that issue.”

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    Strictly output in the JSON format defined by `SpecialistOutput`, and in `draft_modifications` clearly specify the exact SOAP field to which each item of information belongs, for example: - “Write ‘chest pain for 2 hours’ into `history_of_present_illness`” - “Write ‘blood pressure 150/90 mmHg’ into `physical_examination`”

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    Further evaluation: - Planned tests, purpose, and timing, with guideline-based rationale - If surgery is indicated, include indication and approach

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    Non-drug management: - Hydration goals, diet, lifestyle advice, pain management, and warning signs

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    Follow-up: - Follow-up timeframe, repeat tests, and visit format (in-person or remote) soap_guidance: Aggregator You are a recorder and decision-maker in a medical MDT. **Current phase: {phase_name}** As a professional physician, you must organize and document patient information strictly according to the standard SOAP format for the initial medical recor...

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    **If `specialist_outputs` is empty, extract information directly from the patient’s latest response and the coordinator’s suggestions, and update the SOAP record accordingly.**

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    **Empathy**: You are the doctor directly communicating with the patient; your tone should be warm, professional, and patient

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    If the patient has clearly stated that certain information cannot be provided, document this truthfully and do not ask again

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    Physical Examination 2 2.1 Complete Physical Exam Comprehensive documentation of physical examination. 4 Errors/not addressed Major components missing Mostly complete; minor omissions Complete exam Well-organized; professional terms 2 2.2 Key Physical Findings Highlights diagnostically relevant positive/negative findings. 3 Missing/incorrect emphasis Part...

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    Diagnosis & Differential 3 3.1 Diagnostic Completeness Primary, secondary, and additional diagnoses. 4 Primary missing/incorrect Primary only Primary + some secondary Primary + all secondary Includes additional diagnoses 3 3.2 Objective Evidence Evidence from history, exam, and investigations. 4 Missing/incorrect evidence One domain Two domains All releva...

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    Plan 4 4.1 Plan Completeness Investigations, treatment, lifestyle, follow-up. 4 Missing/incorrect Single aspect Multi-dimensional Dynamic assess- ment/prognosis Concise, rigorous 4 4.2 Plan Appropriateness Evidence/reasoning supports key decisions. 3 Inappropriate/incorrectVague/unsupported Generally appropriate Clear; strong evidence –

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    3 – Basic: partial Good: most Excellent: nearly all – 5 5.2 Reasoning Skill Quality of diagnostic reasoning

    Overall Competency 5 5.1 Presentation Skill Quality of written presentation. 3 – Basic: partial Good: most Excellent: nearly all – 5 5.2 Reasoning Skill Quality of diagnostic reasoning. 3 – Basic reasoning Relevant comparison Comprehensive, rigorous – 5 5.3 Decision Skill Quality of decisions in the plan. 3 – List actions only Partial reasoning Evidence-b...

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    **Physical examination:** Conscious and alert, in fair general condition. Pain score: 0. Respiratory rate: 18 breaths/min; Oral temperature: 36.7°C; Pulse: 69 beats/min; Blood pressure: 131/71 mmHg. No cyanosis of the lips. No palpable superficial lymphadenopathy. Cardiac and pulmonary auscultation revealed no significant abnormalities. Abdomen soft, with...

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    *Right inner prostate:* 3 cores obtained; prostate cancer in all 3 cores; Gleason score 4+3=7; tumor involvement 20%

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    *Right outer prostate:* 3 cores obtained; prostate cancer in all 3 cores; Gleason score 5+3=8; tumor involvement 60%

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    ------ ## Preliminary Diagnosis

    *Suspicious area of left outer prostate:* 3 cores obtained; prostate cancer in 2 cores; Gleason score 5+3=8; tumor involvement 70%. ------ ## Preliminary Diagnosis

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