Semin Neurol
DOI: 10.1055/a-2772-7189
Review Article

Artificial Intelligence-Enabled Devices in Neurology: Mapping the Present and Future

Authors

  • Ashwin Amurthur

    1   Department of Neurology, Mass General Brigham, Boston, Massachusetts, United States
    2   Harvard Medical School, Boston, Massachusetts, United States
  • Davis J. McCarthy

    3   Bioinformatics and Cellular Genomics, St. Vincent's Institute of Medical Research, Melbourne, Victoria, Australia
    4   Department of Medicine, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Victoria, Australia
  • Lee H. Schwamm

    5   Departments of Bioinformatics and Data Science, and of Neurology, Division of Vascular Neurology, Yale School of Medicine, New Haven, Connecticut, United States
  • James M. Hillis

    1   Department of Neurology, Mass General Brigham, Boston, Massachusetts, United States
    2   Harvard Medical School, Boston, Massachusetts, United States
    6   Digital Clinical Research Organization, Mass General Brigham AI, Mass General Brigham, Boston, Massachusetts, United States

Abstract

Over the last decade, there has been a rapid expansion in medical devices utilizing artificial intelligence (AI) and machine learning (ML), with a growing role in neurologic care. These devices are beginning to augment clinical workflows and reshape how neurologists engage with technology to deliver patient care. In this review, we first introduce core ML techniques that are used within devices. We then describe the AI-enabled medical devices that have received U.S. Food and Drug Administration authorization as of December 31, 2024, including an analysis of the 147 devices across neuroradiology and broader neurology indications. We also highlight key trends in how these devices are being integrated into clinical practice. We conclude by examining emerging models of human–machine interaction and their implications for future neurologic care.



Publication History

Received: 27 November 2025

Accepted: 15 December 2025

Accepted Manuscript online:
16 December 2025

Article published online:
08 January 2026

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