Agentic AI for scaling diagnosis and care in neurodegenerative disease - Nature

Abstract

US healthcare systems are struggling to meet the growing demand for neurological care, particularly in Alzheimer’s disease and related dementias. Generative artificial intelligence (AI) built on large language models now enables agentic AI systems that can streamline clinical workflows, integrate multimodal data and learn from practicing specialists. We envision an agentic AI system that scales specialist-level care to nonspecialist clinical settings through a continuously learning healthcare system. We describe this destination and outline a phased roadmap for responsible design and integration into care of Alzheimer’s disease and related dementias: (1) high-quality standardized data collection across modalities; (2) decision support; (3) clinical integration enhancing workflows; (4) rigorous validation and monitoring protocols; (5) continuous learning through clinical feedback; and (6) robust ethics and risk management frameworks. This human-centered approach optimizes clinicians’ capabilities in comprehensive data collection, interpretation of complex clinical information and timely application of relevant medical knowledge while prioritizing patient safety, healthcare equity and transparency.

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Acknowledgements

A.G.B. is supported by the Alzheimer’s Association, Eisai and American Brain Foundation. P.V.H. and R.Z. are supported by NSF grant 2112533.

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A.M.R reports receiving research funding from GE Healthcare. A.I.L. serves as a consultant and receives fees and/or stock from EmTheraPro (co-founder), NextSense, Alamar, Cognition Therapeutics, Cognito, Eratos and Asha. The other authors declare no competing interests.

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Breithaupt, A.G., Weiner, M., Tang, A. et al. Agentic AI for scaling diagnosis and care in neurodegenerative disease. Nat Aging (2026). https://doi.org/10.1038/s43587-026-01186-z

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