基于强化优化大语言模型推理的神经退行性痴呆可解释诊断框架

An Explainable Diagnostic Framework for Neurodegenerative Dementias via Reinforcement-Optimized LLM Reasoning

May 26, 2025

作者: Andrew Zamai, Nathanael Fijalkow, Boris Mansencal, Laurent Simon, Eloi Navet, Pierrick Coupe

cs.AI

摘要

神经退行性痴呆的鉴别诊断是一项极具挑战性的临床任务，主要源于症状表现的重叠以及结构神经影像中观察到的模式相似性。为提高诊断效率和准确性，基于深度学习的方法，如卷积神经网络和视觉Transformer，已被提出用于脑部MRI的自动分类。然而，尽管这些模型具有强大的预测性能，但由于其决策过程的不透明性，在临床应用中受到限制。在本研究中，我们提出了一种整合两大核心组件的框架，以增强诊断的透明度。首先，我们引入了一个模块化流程，将3D T1加权脑部MRI转换为放射学报告文本。其次，我们探索了现代大型语言模型（LLMs）在基于生成报告进行额颞叶痴呆亚型、阿尔茨海默病与正常衰老之间鉴别诊断中的辅助潜力。为弥合预测准确性与可解释性之间的鸿沟，我们采用强化学习激励LLMs进行诊断推理。无需监督推理轨迹或从更大模型蒸馏，我们的方法促使基于神经影像发现的结构化诊断理由自然涌现。与事后解释方法回顾性地为模型决策提供辩护不同，我们的框架在推理过程中生成诊断理由，产生因果基础的解释，这些解释不仅告知还指导模型的决策过程。通过这种方式，我们的框架在保持现有深度学习方法诊断性能的同时，提供了支持其诊断结论的推理依据。

English

The differential diagnosis of neurodegenerative dementias is a challenging clinical task, mainly because of the overlap in symptom presentation and the similarity of patterns observed in structural neuroimaging. To improve diagnostic efficiency and accuracy, deep learning-based methods such as Convolutional Neural Networks and Vision Transformers have been proposed for the automatic classification of brain MRIs. However, despite their strong predictive performance, these models find limited clinical utility due to their opaque decision making. In this work, we propose a framework that integrates two core components to enhance diagnostic transparency. First, we introduce a modular pipeline for converting 3D T1-weighted brain MRIs into textual radiology reports. Second, we explore the potential of modern Large Language Models (LLMs) to assist clinicians in the differential diagnosis between Frontotemporal dementia subtypes, Alzheimer's disease, and normal aging based on the generated reports. To bridge the gap between predictive accuracy and explainability, we employ reinforcement learning to incentivize diagnostic reasoning in LLMs. Without requiring supervised reasoning traces or distillation from larger models, our approach enables the emergence of structured diagnostic rationales grounded in neuroimaging findings. Unlike post-hoc explainability methods that retrospectively justify model decisions, our framework generates diagnostic rationales as part of the inference process-producing causally grounded explanations that inform and guide the model's decision-making process. In doing so, our framework matches the diagnostic performance of existing deep learning methods while offering rationales that support its diagnostic conclusions.