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arXiv · 2508.08115

TeamMedAgents: Pareto-Efficient Multi-Agent Medical Reasoning Through Teamwork Theory

Pranav Pushkar Mishra·Mohammad Arvan·Mohan Zalake
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Abstract

Complex medical reasoning has historically required frontier language models to achieve clinically-acceptable accuracy, creating computational barriers that limit deployment in resource-constrained clinical settings. We present TeamMedAgents, a modular multi-agent framework that translates Salas et al.'s evidence-based teamwork theory into computational mechanisms--shared mental models, team leadership, team orientation, trust networks, and mutual monitoring--enabling Small Language Models to perform multi-step clinical reasoning efficiently. Evaluation across 8 medical benchmarks demonstrates that TeamMedAgents advances the Pareto efficiency frontier by 1-2 orders of magnitude, achieving competitive accuracy at substantially lower token cost than MDAgents, MedAgents, DyLAN, and ReConcile. The framework exhibits the lowest cross-dataset variance among multi-agent approaches, enabling deployment without per-task tuning. Our results establish that theory-grounded coordination mechanisms provide essential scaffolding for deploying efficient medical AI in resource-constrained clinical environments.

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Pranav Pushkar Mishra, Mohammad Arvan, Mohan Zalake. 2025-08-11. TeamMedAgents: Pareto-Efficient Multi-Agent Medical Reasoning Through Teamwork Theory. https://arxiv.org/abs/2508.08115

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