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Semin Neurol
DOI: 10.1055/a-2744-9871
Review Article

Review of Artificial Intelligence for Clinical Use in Alzheimer's Disease and Related Dementias

Authors

  • Andrew G. Breithaupt

    1   Department of Neurology, Emory University School of Medicine, Emory Goizueta Brain Health Institute, Atlanta, Georgia, United States

  • Alice Tang

    2   Bakar Computational Health Sciences Institute and School of Medicine, University of California, San Francisco, San Francisco, California, United States

  • Emily W. Paolillo

    3   Department of Neurology, Memory and Aging Center, Weill Institute for Neuroscience, University of California, San Francisco, California, United States

  • Merna Bibars

    4   Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States

  • Erik C. B. Johnson

    1   Department of Neurology, Emory University School of Medicine, Emory Goizueta Brain Health Institute, Atlanta, Georgia, United States

  • Rowan Saloner

    3   Department of Neurology, Memory and Aging Center, Weill Institute for Neuroscience, University of California, San Francisco, California, United States

  • Katherine L. Possin

    3   Department of Neurology, Memory and Aging Center, Weill Institute for Neuroscience, University of California, San Francisco, California, United States
    5   Department of Neurology, Global Brain Health Institute, University of California, San Francisco, California, United States

  • Charles C. Windon

    3   Department of Neurology, Memory and Aging Center, Weill Institute for Neuroscience, University of California, San Francisco, California, United States

  • Tanisha G. Hill-Jarrett

    3   Department of Neurology, Memory and Aging Center, Weill Institute for Neuroscience, University of California, San Francisco, California, United States
    5   Department of Neurology, Global Brain Health Institute, University of California, San Francisco, California, United States

  • Joseph Giorgio

    6   Department of Neuroscience, University of California, Berkeley, Berkeley, California, United States

  • Andreas M. Rauschecker

    7   Department of Radiology and Biomedical Imaging, Center for Intelligent Imaging (ci2), University of California, San Francisco, San Francisco, California, United States

  • Hyeokhyen Kwon

    4   Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States
    8   Department of Biomedical Informatics, Emory University, Atlanta, Georgia, United States

  • Jet M. J. Vonk

    3   Department of Neurology, Memory and Aging Center, Weill Institute for Neuroscience, University of California, San Francisco, California, United States

  • Pedro Pinheiro-Chagas

    3   Department of Neurology, Memory and Aging Center, Weill Institute for Neuroscience, University of California, San Francisco, California, United States

Funding Information Research reported in this publication was supported by the National Institute on Aging of the National Institutes of Health under award number K23AG093166. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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Abstract

As the U.S. population ages, Alzheimer's disease and related dementias (ADRD) cases are increasing, resulting in long wait times for specialist care. We review state-of-the-art artificial intelligence (AI) applications in ADRD care, from streamlining clinical diagnosis to pioneering novel digital biomarkers. Near-term AI applications include neuroimaging interpretation, conversational agents for patient interviews, and digital cognitive assessments. Large language models show promise as collaborative partners, helping clinicians interpret complex data while supporting patients and caregivers. Emerging digital biomarkers—speech analysis, passive monitoring through wearable devices, electronic health record analysis, and multiomics—offer potential for continuous monitoring to detect cognitive decline years before traditional assessments. Despite the acceleration of AI innovation, most of these systems are inaccessible in clinical practice. Implementation bottlenecks include limited external validation, technical challenges, model biases, infrastructure, and regulatory requirements. This review aims to help neurologists navigate this rapidly evolving AI landscape and prepare for implementation in ADRD care.

Keywords

AI - ML - neurodegenerative disease - ADRD - digital biomarkers


Publication History

Received: 05 August 2025

Accepted: 12 November 2025

Article published online:
28 November 2025

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