ArticleNPJ digital medicine2025
CARE-AD: a multi-agent large language model framework for Alzheimer's disease prediction using longitudinal clinical notes.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07318701 (Multi-agent Large Language Models for Multidisciplinary Decision Support in Cervical Cancer During Pregnancy), which is not on this map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Multi-agent Large Language Models for Multidisciplinary Decision Support in Cervical Cancer During Pregnancy
Who cites it
15 citing papers in PubMed.
- CPS-Net (Collaborating Physicians in Silico Network): A Decentralized Multi-Agent Transformer Framework for Specialty-Aware Disease Prediction.Journal of medical systems · 2026Article
- A large language model-driven multidisciplinary AI agent system predicts delirium in emergency critically ill patients.Cell reports. Medicine · 2026Article
- Generative large language models in medicine: a scoping review of recent methodological advances.npj health systems · 2026Review
- MediHive: A Decentralized Agent Collective for Medical Reasoning.Journal of healthcare informatics research · 2026Article
- Review
- A Multiagent Large Language Model Framework for Emergency Treatment Recommendation in Acute Ischemic Stroke: Development and Validation Study.Journal of medical Internet research · 2026Article
- A comprehensive survey of AI agents in healthcare.Journal of biomedical informatics · 2026Review
- PHO-Agents: A Large Language Model-Powered Multi-Agent System for Predicting Health Outcomes.medRxiv : the preprint server for health sciences · 2026Article
- Evaluation Frameworks for Clinical Foundation Models in Specific Tasks of Unstructured Medical Text Analysis: A Scoping Review.Healthcare (Basel, Switzerland) · 2026Review
- Explainable Patient-Level Cognitive Impairment Screening via Temporal, Semantic, and Psycholinguistic Multimodal AI.Journal of Intelligence · 2026Article
- Benchmarking large language model-based agent systems for clinical decision tasks.NPJ digital medicine · 2026Article
- Article
- Precision oncology: from large language models to multi-agent systems.Frontiers in oncology · 2026Review
- Integrating Electronic Health Records and Large Language Models for Coarse-to-Fine Hybrid Disease Prediction.Health data science · 2026Article
- Locally deployed large language model for real-world lecanemab eligibility pre-screening.Alzheimer's & dementia (Amsterdam, Netherlands)Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Large language models (LLMs) have shown promising capabilities across diverse domains, yet their application to complex clinical prediction tasks remains limited. In this study, we present CARE-AD (Collaborative Analysis and Risk Evaluation for Alzheimer's Disease), a multi-agent LLM-based framework for forecasting Alzheimer's disease (AD) onset by analyzing longitudinal electronic health record (EHR) notes. CARE-AD assigns specialized LLM agents to extract signs and symptoms relevant to AD and conduct domain-specific evaluations-emulating a collaborative diagnostic process. In a retrospective evaluation, CARE-AD achieved higher accuracy (0.53 vs. 0.26-0.45) than baseline single-model approaches in predicting AD risk 10 years prior to the first recorded diagnosis code. These findings highlight the feasibility of using multi-agent LLM systems to support early risk assessment for AD and motivate further research on their integration into clinical decision support workflows.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.