Chain-of-Thought Reasoning with Large Language Models for Clinical Alzheimer's Disease Assessment and Diagnosis

2026-02-15

AI Summary

This study addresses the clinical challenge of Alzheimer’s disease diagnosis, which traditionally relies on time-consuming and resource-intensive imaging and cognitive assessments, and lacks efficient, interpretable automated support tools. To bridge this gap, the work introduces Chain-of-Thought (CoT) reasoning into automated diagnosis for the first time, leveraging large language models to perform structured analysis of electronic health records. This approach generates explicit, human-interpretable diagnostic rationales alongside predictions, significantly enhancing the model’s capacity to understand multifactorial etiology and accurately determine disease staging. Evaluated on multiple Clinical Dementia Rating (CDR) classification tasks, the method achieves up to a 15% improvement in F1 score over zero-shot baselines, demonstrating both superior performance and enhanced clinical transparency.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Reasoning under Uncertainty: Causality

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Alzheimer's disease (AD) has become a prevalent neurodegenerative disease worldwide. Traditional diagnosis still relies heavily on medical imaging and clinical assessment by physicians, which is often time-consuming and resource-intensive in terms of both human expertise and healthcare resources. In recent years, large language models (LLMs) have been increasingly applied to the medical field using electronic health records (EHRs), yet their application in Alzheimer's disease assessment remains limited, particularly given that AD involves complex multifactorial etiologies that are difficult to observe directly through imaging modalities. In this work, we propose leveraging LLMs to perform Chain-of-Thought (CoT) reasoning on patients'clinical EHRs. Unlike direct fine-tuning of LLMs on EHR data for AD classification, our approach utilizes LLM-generated CoT reasoning paths to provide the model with explicit diagnostic rationale for AD assessment, followed by structured CoT-based predictions. This pipeline not only enhances the model's ability to diagnose intrinsically complex factors but also improves the interpretability of the prediction process across different stages of AD progression. Experimental results demonstrate that the proposed CoT-based diagnostic framework significantly enhances stability and diagnostic performance across multiple CDR grading tasks, achieving up to a 15% improvement in F1 score compared to the zero-shot baseline method.

Problem

• Alzheimer's disease
• clinical diagnosis
• large language models
• electronic health records
• multifactorial etiologies

Innovation

• Chain-of-Thought Reasoning
• Large Language Models
• Alzheimer's Disease Diagnosis
• Electronic Health Records
• Interpretable AI