{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T16:18:07Z","timestamp":1788279487462,"version":"build-2803163510"},"reference-count":60,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T00:00:00Z","timestamp":1767571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Aging Neurosci."],"abstract":"<jats:p>Neurodegenerative diseases such as Alzheimer\u2019s and Parkinson\u2019s 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\u2019s current role in Alzheimer\u2019s care, identifies key challenges in integration, interpretability, and regulation, and explores pathways for safely deploying these agentic systems in clinical practice.<\/jats:p>","DOI":"10.3389\/fnagi.2025.1735892","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T06:37:53Z","timestamp":1767595073000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["AI agents in Alzheimer\u2019s disease management: challenges and future directions"],"prefix":"10.3389","volume":"17","author":[{"given":"Gerasimos","family":"Grammenos","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aristidis G.","family":"Vrahatis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Konstantinos","family":"Lazaros","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Themis P.","family":"Exarchos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Panagiotis","family":"Vlamos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marios G.","family":"Krokidis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,1,5]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"10.31478\/202209c","DOI":"10.31478\/202209c","article-title":"Meeting the moment: addressing barriers and facilitating clinical adoption of artificial intelligence in medical diagnosis.","volume":"2022","author":"Adler-Milstein","year":"2022","journal-title":"NAM Perspect"},{"key":"B2","doi-asserted-by":"publisher","first-page":"fcae208","DOI":"10.1093\/braincomms\/fcae208","article-title":"Predicting conversion from mild cognitive impairment to Alzheimer\u2019s disease: a multimodal approach.","volume":"6","author":"Agostinho","year":"2024","journal-title":"Brain Commun"},{"key":"B3","doi-asserted-by":"publisher","first-page":"e46454","DOI":"10.7759\/cureus.46454","article-title":"A systematic review of the barriers to the implementation of artificial intelligence in healthcare.","volume":"15","author":"Ahmed","year":"2023","journal-title":"Cureus"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1596632","DOI":"10.3389\/fneur.2025.1596632","article-title":"Research progress in predicting the conversion from mild cognitive impairment to Alzheimer\u2019s disease via multimodal MRI and artificial intelligence.","volume":"16","author":"Ai","year":"2025","journal-title":"Front. 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