AD-Reasoning: Multimodal Guideline-Guided Reasoning for Alzheimer's Disease Diagnosis
AI 摘要
AD-Reasoning提出了一种基于多模态信息的、结合NIA-AA指南的阿尔茨海默病诊断框架，提高了诊断准确性和透明性。
主要贡献
- 提出了AD-Reasoning多模态诊断框架
- 构建了AD-MultiSense多模态QA数据集
- 利用规则验证器和强化学习保证诊断符合NIA-AA指南
方法论
结合MRI和临床数据，利用特定模态编码器和交叉注意力融合，通过强化学习进行微调，并使用规则验证器。
原文摘要
Alzheimer's disease (AD) diagnosis requires integrating neuroimaging with heterogeneous clinical evidence and reasoning under established criteria, yet most multimodal models remain opaque and weakly guideline-aligned. We present AD-Reasoning, a multimodal framework that couples structural MRI with six clinical modalities and a rule-based verifier to generate structured, NIA-AA-consistent diagnoses. AD-Reasoning combines modality-specific encoders, bidirectional cross-attention fusion, and reinforcement fine-tuning with verifiable rewards that enforce output format, guideline evidence coverage, and reasoning--decision consistency. We also release AD-MultiSense, a 10,378-visit multimodal QA dataset with guideline-validated rationales built from ADNI/AIBL. On AD-MultiSense, AD-Reasoning achieves state-of-the-art diagnostic accuracy and produces structured rationales that improve transparency over recent baselines, while providing transparent rationales.