医学影像问答新方法，让AI像专家会诊一样推理。
MedCoT: Medical Chain of Thought via Hierarchical Expert
- 分层专家协作：先出诊断思路，再验证，最后投票定结论。
- 在4个标准数据集上超越现有模型，准确率显著提升。
- 适合医疗AI可解释性要求高的临床场景使用。
人工智能在医学视觉问答（Med-VQA）领域取得进展，但多数研究仅关注答案准确性，忽视了推理过程和可解释性，而这在临床环境中至关重要。此外，现有Med-VQA算法多依赖单一模型，缺乏真实医疗诊断所需的多专家协同评估的鲁棒性。为此，本文提出MedCoT——一种基于分层专家验证的推理链方法，旨在提升生物医学影像问答的准确性和可解释性。该方法基于两个原则：显式推理路径的必要性，以及多专家评审以得出准确结论的需求。具体流程为：初始专家提出诊断依据，后续专家进行验证，最终由本地部署的稀疏专家混合模型中的诊断专家通过投票达成共识，给出最终诊断。在四个标准Med-VQA数据集上的实验表明，MedCoT显著优于现有最先进方法，在性能与可解释性方面均有明显提升。
原文摘要 · Abstract (English)
Artificial intelligence has advanced in Medical Visual Question Answering (Med-VQA), but prevalent research tends to focus on the accuracy of the answers, often overlooking the reasoning paths and interpretability, which are crucial in clinical settings. Besides, current Med-VQA algorithms, typically reliant on singular models, lack the robustness needed for real-world medical diagnostics which usually require collaborative expert evaluation. To address these shortcomings, this paper presents MedCoT, a novel hierarchical expert verification reasoning chain method designed to enhance interpretability and accuracy in biomedical imaging inquiries. MedCoT is predicated on two principles: The necessity for explicit reasoning paths in Med-VQA and the requirement for multi-expert review to formulate accurate conclusions. The methodology involves an Initial Specialist proposing diagnostic rationales, followed by a Follow-up Specialist who validates these rationales, and finally, a consensus is reached through a vote among a sparse Mixture of Experts within the locally deployed Diagnostic Specialist, which then provides the definitive diagnosis. Experimental evaluations on four standard Med-VQA datasets demonstrate that MedCoT surpasses existing state-of-the-art approaches, providing significant improvements in performance and interpretability.
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