AD-CARE: A Guideline-grounded, Modality-agnostic LLM Agent for Real-world Alzheimer's Disease Diagnosis with Multi-cohort Assessment, Fairness Analysis, and Reader Study

2026-03-26

AI Summary

This study addresses key challenges in the clinical diagnosis of Alzheimer’s disease, including missing and heterogeneous multimodal data as well as performance disparities across demographic subgroups, which hinder existing large language models (LLMs) from generating clinically reliable reports. To overcome these limitations, the authors propose AD-CARE—a modality-agnostic, clinical guideline–driven LLM agent that dynamically invokes specialized diagnostic tools to produce transparent, structured diagnostic reports without requiring imputation of missing modalities. By embedding clinical guidelines directly into the LLM’s reasoning process, AD-CARE achieves significantly improved diagnostic accuracy (84.9% across six cohorts totaling 10,303 cases, representing a relative gain of 4.2%–13.7%), reduces performance gaps across racial and age subgroups by 21%–68%, and enhances real-world clinical practice by boosting physician diagnostic accuracy by 6%–11% while halving decision-making time.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: AI for Accessibility
• Computer Vision: Multi-modal Vision

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Alzheimer's disease (AD) is a growing global health challenge as populations age, and timely, accurate diagnosis is essential to reduce individual and societal burden. However, real-world AD assessment is hampered by incomplete, heterogeneous multimodal data and variability across sites and patient demographics. Although large language models (LLMs) have shown promise in biomedicine, their use in AD has largely been confined to answering narrow, disease-specific questions rather than generating comprehensive diagnostic reports that support clinical decision-making. Here we expand LLM capabilities for clinical decision support by introducing AD-CARE, a modality-agnostic agent that performs guideline-grounded diagnostic assessment from incomplete, heterogeneous inputs without imputing missing modalities. By dynamically orchestrating specialized diagnostic tools and embedding clinical guidelines into LLM-driven reasoning, AD-CARE generates transparent, report-style outputs aligned with real-world clinical workflows. Across six cohorts comprising 10,303 cases, AD-CARE achieved 84.9% diagnostic accuracy, delivering 4.2%-13.7% relative improvements over baseline methods. Despite cohort-level differences, dataset-specific accuracies remain robust (80.4%-98.8%), and the agent consistently outperforms all baselines. AD-CARE reduced performance disparities across racial and age subgroups, decreasing the average dispersion of four metrics by 21%-68% and 28%-51%, respectively. In a controlled reader study, the agent improved neurologist and radiologist accuracy by 6%-11% and more than halved decision time. The framework yielded 2.29%-10.66% absolute gains over eight backbone LLMs and converges their performance. These results show that AD-CARE is a scalable, practically deployable framework that can be integrated into routine clinical workflows for multimodal decision support in AD.

Problem

• Alzheimer's disease
• multimodal diagnosis
• clinical decision support
• data heterogeneity
• diagnostic fairness

Innovation

• modality-agnostic
• guideline-grounded reasoning
• fairness-aware diagnosis
• multimodal clinical decision support
• large language model agent