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Agentic AI for scaling diagnosis and care in neurodegenerative disease
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DOI：10.1038/s43587-026-01186-z.png)
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Abstract
En 中文
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. In this Perspective, the authors envision that agentic AI systems may in the future support clinical workflows for neurodegenerative disease diagnosis and care. They propose a system for scaling specialist-level dementia care via synergistic collaborations between humans and an AI-driven continuously learning healthcare system that can reason across all patient data.
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