Semin Neurol
DOI: 10.1055/a-2753-6166
Review Article

The Role of Artificial Intelligence in Deep Brain Stimulation

Authors

  • Tejas R. Mehta

    1   Department of Neurology, Norman Fixel Institute for Neurological Disease, University of Florida, Gainesville, Florida, United States
  • Venkat S. Lavu

    2   Department of Neurology, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States
  • Hao Gao

    3   Department of Medicine, King's College Hospital, Denmark Hill, London, United Kingdom
  • Tania Banerjee

    4   Department of Information Science Technology, Cullen College of Engineering, University of Houston, Houston, Texas, United States
  • Renjie Hu

    4   Department of Information Science Technology, Cullen College of Engineering, University of Houston, Houston, Texas, United States
  • Ruogu Fang

    5   J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, Florida, United States
  • Joshua K. Wong

    1   Department of Neurology, Norman Fixel Institute for Neurological Disease, University of Florida, Gainesville, Florida, United States

Abstract

Deep brain stimulation (DBS) is a highly effective treatment for movement disorders like Parkinson's disease, essential tremor, and dystonia. However, the current multidisciplinary workflow for implanting and programming DBS is often complex, which can lead to improperly placed leads, suboptimal symptom management, and increased procedure time, ultimately resulting in poor patient outcomes. There is a pressing need for a more streamlined, accurate, reproducible, and personalized approach to DBS therapy. Artificial intelligence (AI), which can analyze complex data and identify patterns with remarkable speed, holds significant promise as a tool to address these challenges. This narrative review explores the current and future applications of AI in improving the entire DBS workflow, from surgical planning and lead placement to postoperative programming, with the goal of enhancing clinical efficiency and achieving better, more personalized outcomes for patients with movement disorders.



Publication History

Received: 31 August 2025

Accepted: 11 November 2025

Accepted Manuscript online:
25 November 2025

Article published online:
12 December 2025

© 2025. Thieme. All rights reserved.

Thieme Medical Publishers, Inc.
333 Seventh Avenue, 18th Floor, New York, NY 10001, USA