医学AI未来不在通用模型，而在多智能体协作。
Why Specialist Models Still Matter: A Heterogeneous Multi-Agent Paradigm for Medical Artificial Intelligence
- 构建异构多智能体框架，融合通用大模型与专科模型。
- 实验证明协同效果优于单一模型，显著提升决策准确率。
- 适合医疗AI系统设计者与临床研究者参考。
通用大语言模型（如GPT、Claude）在医疗领域的出色表现引发关键问题：领域专用的医学专家模型是否将被淘汰？我们认为，医疗人工智能的未来不在于打造单一的医学基础模型，也不在于取代人类专家，而在于协调通用大模型、领域专用专家模型与临床医生之间的协作。本文提出HetMedAgent——一种异构医学多智能体框架，支持冲突感知的证据融合、基于不确定性的临床干预触发以及自适应阈值校准。在三个真实世界临床决策任务上的实验表明，通用大模型与领域专用模型的协同显著优于单独使用任一类型模型，验证了专家模型在模态特异性分析中的不可替代性。HetMedAgent标志着从构建医学大模型转向多智能体协作的范式转变，实现了通用推理能力与领域精确性的平衡。
原文摘要 · Abstract (English)
The impressive performance of generalist large language models (LLMs) such as GPT and Claude in healthcare raises a critical question: will domain-specific medical specialist models become obsolete? We argue that the future of medical artificial intelligence (AI) lies not in building monolithic medical foundation models, nor in replacing human expertise, but in orchestrating collaboration among generalist LLMs, domain-specific specialist models, and clinicians. We propose HetMedAgent, a heterogeneous medical multi-agent framework that enables conflict-aware evidence fusion, uncertainty-based clinician intervention triggering, and adaptive threshold calibration. Experiments on three real-world clinical decision-making tasks demonstrate that the synergy between generalist LLMs and domain-specific specialist models significantly outperforms using either type of model alone, validating the irreplaceable value of specialist models in modality-specific analysis. HetMedAgent represents a shift from building medical LLMs or foundation models to multi-agent collaboration, achieving a balance between general reasoning capabilities and domain-specific precision.
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