One Panel Does Not Fit All: Case-Adaptive Multi-Agent Deliberation for Clinical Prediction

TL;DR

CAMP dynamically assembles specialist panels for clinical prediction, improving accuracy and transparency by tailoring expert consensus to each case's diagnostic uncertainty.

Contribution

This work introduces CAMP, a case-adaptive multi-agent framework that enhances clinical prediction by dynamically selecting specialists and incorporating principled abstention and arbitration.

Findings

CAMP outperforms baseline methods in diagnostic accuracy.

CAMP uses fewer tokens than comparable multi-agent approaches.

CAMP provides transparent decision audits with voting and arbitration traces.

Abstract

Large language models applied to clinical prediction exhibit case-level heterogeneity: simple cases yield consistent outputs, while complex cases produce divergent predictions under minor prompt changes. Existing single-agent strategies sample from one role-conditioned distribution, and multi-agent frameworks use fixed roles with flat majority voting, discarding the diagnostic signal in disagreement. We propose CAMP (Case-Adaptive Multi-agent Panel), where an attending-physician agent dynamically assembles a specialist panel tailored to each case's diagnostic uncertainty. Each specialist evaluates candidates via three-valued voting (KEEP/REFUSE/NEUTRAL), enabling principled abstention outside one's expertise. A hybrid router directs each diagnosis through strong consensus, fallback to the attending physician's judgment, or evidence-based arbitration that weighs argument quality over vote counts. On diagnostic prediction and brief hospital course generation from MIMIC-IV across four LLM backbones, CAMP consistently outperforms strong baselines while consuming fewer tokens than most competing multi-agent methods, with voting records and arbitration traces offering transparent decision audits.