ArticleFrontiers in aging neuroscience2025
AI agents in Alzheimer's disease management: challenges and future directions.
Article in Frontiers in aging neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Multi‑feature Prediction Model for Coronary Heart Disease Comorbidity in Middle‑aged and Older Adults with COPD Based on Machine Learning and SHAP.International journal of chronic obstructive pulmonary disease · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neurodegenerative diseases such as Alzheimer's and Parkinson's disease pose a major global healthcare challenge, with cases projected to rise sharply as populations age and effective treatments remain limited. AI has shown promise in supporting diagnostics, predicting disease progression, and exploring biomarkers, yet most current tools are narrowly focused, unimodal, and lack longitudinal reasoning or interpretability. By enabling context-aware analysis across imaging, genomics, cognitive, and behavioral data, agentic AI can track disease progression, identify therapeutic targets, and support clinical decision-making. Over time, these systems may detect gaps in their own information and request targeted data, moving closer to real clinical reasoning while keeping clinicians in control. The next frontier in medical AI lies in developing autonomous, multimodal agents capable of integrating diverse data, adapting through experience, supporting decision-making, and collaborating with clinicians. Furthermore, ethical, patient-centered AI requires close technical-clinical collaboration to support clinicians and improve patient outcomes. This perspective examines AI's current role in Alzheimer's care, identifies key challenges in integration, interpretability, and regulation, and explores pathways for safely deploying these agentic systems in clinical practice.
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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.