MdCoT: Medical Diagnosis Chain-of-Thought with Self-Diagnostic Refinement for Alzheimer's Disease

Chunlin Lu, Yongheng Zhang, Peng Wang, Wenpeng Lu, Libo Qin

Abstract: Accurate diagnosis of Alzheimer’s disease is vital for effective treatment. Recently, a mainstream approach emerged that uses large language models (LLMs) to generate interpretable diagnosis. However, these methods face two significant challenges: (1) Incomplete Utilization of Raw Image Information: Converting images into text for LLMs input leads to the loss of the original image information. (2) Hallucination in LLMs: Diagnostic biases from LLMs hallucinations are neglected. To address these challenges, we propose a Medical Diagnosis Chain-of-Thought with Self-Diagnostic Refinement (MdCoT) framework. The MdCoT framework consists of two core modules: (1) Multimodal AD Diagnostic Chain-of-Thought: By including raw images as input, multimodal large language models (MLLMs) are allowed to capture fine-grained features, and (2) Self-Diagnostic Refinement: By guiding MLLMs to self-check and refine the diagnostic results, MdCoT achieves to mitigate hallucination. Experiments on widely used benchmarks demonstrate that MdCoT surpasses all baselines. Besides, extensive auxiliary experiments demonstrate the superiority of MdCoT.

External IDs:dblp:conf/icmcs/LuZWLQ25