MedCoT: Medical Chain of Thought via Hierarchical Expert

• URL: http://arxiv.org/abs/2412.13736v1
• Date: Wed, 18 Dec 2024 11:14:02 GMT
• Title: MedCoT: Medical Chain of Thought via Hierarchical Expert
• Authors: Jiaxiang Liu, Yuan Wang, Jiawei Du, Joey Tianyi Zhou, Zuozhu Liu,
• Abstract summary: This paper presents MedCoT, a novel hierarchical expert verification reasoning chain method. It is designed to enhance interpretability and accuracy in biomedical imaging inquiries. Experimental evaluations on four standard Med-VQA datasets demonstrate that MedCoT surpasses existing state-of-the-art approaches.
• Score: 48.91966620985221
• License:
• Abstract: 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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