AccScience Publishing / AN / Online First / DOI: 10.36922/AN026160009
EDITORIAL

Artificial intelligence in neurology: Beyond accuracy toward clinical responsibility

Milan Toma1*
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1 Algorithmic Medicine Laboratory, Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, New York, United States of America
Advanced Neurology, 026160009 https://doi.org/10.36922/AN026160009
Received: 16 April 2026 | Published online: 22 May 2026
(This article belongs to the Special Issue Artificial Intelligence Applied to Neurology)
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Conflict of interest
Milan Toma is the Guest Editor of this Special Issue. The author declares that he has no competing interests relevant to the content of this editorial.
References
  1. Husain G, Mayer J, Bekbolatova M, Vathappallil P, Matalia M, Toma M. Machine learning for medical image classification. Acad Med. 2024;1(4). doi: 10.20935/acadmed7444
  2. Hong S, Matalia M, Toma M. Chatting ain’t diagnosing: Diagnostic variability and fundamental errors in multimodal LLM interpretation in radiology. Algorithms. 2026;19(3):170. doi: 10.3390/a19030170
  3. Toma M. AI-Assisted Medical Diagnostics: A Clinical Guide to Next-Generation Diagnostics. New York City, USA: Dawning Research Press. 2025.
  4. Toma M. Diagnosing AI: Evaluation of AI in Clinical Practice. New York City, USA: Dawning Research Press. 2026.
  5. Leming M, Kim K, Bruffaerts R, Im H. Strategies for mitigating data heterogeneities in AI-based neuro-disease detection. Neuron. 2025;113(8):1129-1132. doi: 10.1016/j.neuron.2025.01.028
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Advanced Neurology, Electronic ISSN: 2810-9619 Print ISSN: 3060-8589, Published by AccScience Publishing