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Review
. 2025 Feb 17;7(1):11.
doi: 10.1186/s42466-025-00367-2.

AI and Neurology

Julian Bösel  1   2   3 , Rohan Mathur  4 , Lin Cheng  4 , Marianna S Varelas  5 , Markus A Hobert  6   7 , José I Suarez  4
Affiliations
Review

AI and Neurology

Julian Bösel et al. Neurol Res Pract. .

Abstract

Background: Artificial Intelligence is influencing medicine on all levels. Neurology, one of the most complex and progressive medical disciplines, is no exception. No longer limited to neuroimaging, where data-driven approaches were initiated, machine and deep learning methodologies are taking neurologic diagnostics, prognostication, predictions, decision making and even therapy to very promising potentials.

Main body: In this review, the basic principles of different types of Artificial Intelligence and the options to apply them to neurology are summarized. Examples of noteworthy studies on such applications are presented from the fields of acute and intensive care neurology, stroke, epilepsy, and movement disorders. Finally, these potentials are matched with risks and challenges jeopardizing ethics, safety and equality, that need to be heeded by neurologists welcoming Artificial Intelligence to their field of expertise.

Conclusion: Artificial intelligence is and will be changing neurology. Studies need to be taken to the prospective level and algorithms undergo federated learning to reach generalizability. Neurologists need to master not only the benefits but also the risks in safety, ethics and equity of such data-driven form of medicine.

Keywords: AI; Artificial intelligence; Data-driven medicine; Deep learning; Machine learning; Neural networks; Neurology.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Given by all authors. Competing interests: None.

Figures

Fig. 1
Fig. 1
Framework for development of clinically validated and generalizable AI tools. Suggested steps in AI algorithm development, testing, and validation. Of note, step 5 should be preceded by validation of the algorithm in a data set other than the derivation data set (“external validation”) which may also be done retrospectively, preferably within datasets from different institutions. Prospective validation may be one type of in-house validation
See this image and copyright information in PMC

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