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Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist

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Abstract

Purpose of Review

Machine Learning (ML) and Artificial Intelligence (AI) are data-driven techniques to translate raw data into applicable and interpretable insights that can assist in clinical decision making. Some of these tools have extremely promising initial results, earning both great excitement and creating hype. This non-technical article reviews recent developments in ML/AI in epilepsy to assist the current practicing epileptologist in understanding both the benefits and limitations of integrating ML/AI tools into their clinical practice.

Recent Findings

ML/AI tools have been developed to assist clinicians in almost every clinical decision including (1) predicting future epilepsy in people at risk, (2) detecting and monitoring for seizures, (3) differentiating epilepsy from mimics, (4) using data to improve neuroanatomic localization and lateralization, and (5) tracking and predicting response to medical and surgical treatments. We also discuss practical, ethical, and equity considerations in the development and application of ML/AI tools including chatbots based on Large Language Models (e.g., ChatGPT).

Summary

ML/AI tools will change how clinical medicine is practiced, but, with rare exceptions, the transferability to other centers, effectiveness, and safety of these approaches have not yet been established rigorously. In the future, ML/AI will not replace epileptologists, but epileptologists with ML/AI will replace epileptologists without ML/AI.

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Funding

Dr. Kerr’s research time was supported by the United States National Institutes of Neurological Disorders and Stroke (grant numbers NIH R25 NS089450, NIH U24NS107158); the Susan S. Spencer Clinical Research Training Scholarship funded by the American Academy of Neurology, American Epilepsy Society, Epilepsy Foundation, American Brain Foundation; and the Epilepsy Study Consortium Mini grant.

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  1. Department of Neurology, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA

    Wesley T. Kerr & Katherine N. McFarlane

  2. Department of Biomedical Informatics, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA

    Wesley T. Kerr

  3. Department of Neurology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA

    Wesley T. Kerr

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W.K. wrote the first draft. W.K. and K.M. collaboratively modified the manuscript to reach the final version. All authors approved of the final version.

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Dr. Kerr writes review articles for Medlink Neurology; has paid consulting agreements with SK Life Science, Janssen, Biohaven Pharmaceutical, and Radius Health; and has unpaid research agreements with UCB, GSK, Johnson & Johnson, Eisai, and Jazz Pharmaceuticals. These companies had no part in the production of this manuscript. Ms. McFarlane has no relevant disclosures.

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Kerr, W.T., McFarlane, K.N. Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist. Curr Neurol Neurosci Rep 23, 869–879 (2023). https://doi.org/10.1007/s11910-023-01318-7

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