Artificial Intelligence in Neurology: A Critical Narrative Review of Clinical Evidence, Translation, and Responsible Implementation
- 1Department of Medicine Faculty of Medicine and Health Sciences An‐Najah National University Nablus Palestine
- 2Hurley Medical Center‐MSU Flint Michigan USA
- 3Department of Neurology Mercy St. Vincent Medical Center Toledo Ohio USA
Abstract
Purpose: Artificial intelligence (AI) is increasingly applied to neuroimaging, electrophysiology, clinical text, and digitally measured behavior, but reported performance often exceeds the maturity of clinical evidence.
Method: We conducted a structured narrative review, informed by SANRA principles, of English‐language human studies, reviews, and regulatory sources identified in PubMed/MEDLINE and IEEE Xplore from January 2022 through July 2026; older landmark studies were retained when necessary for historical or methodological context. We applied a common translational‐evidence framework across six prespecified neurological domains to distinguish technical performance from external validation, prospective clinical evaluation, implementation, and demonstrated patient benefit. Evidence was appraised according to cohort representativeness, patient‐level data separation, external and prospective validation, calibration, clinical comparators, regulatory status, workflow integration, and patient‐level benefit.
Finding: Across stroke, dementia, Parkinson disease, epilepsy, multiple sclerosis, and neuro‐oncology, the strongest evidence concerns narrowly defined imaging and signal‐processing tasks. A cluster‐randomized stroke study demonstrated shorter thrombectomy workflow time, and several externally validated models show improved portability. In contrast, many high‐accuracy classification and segmentation studies remain retrospective, single‐dataset, internally validated, and uncalibrated. Multimodal learning, foundation models, federated learning, and large language models broaden capability but introduce additional risks related to dataset shift, bias, opacity, privacy, and automation.
Conclusion: Neuro‐AI should be judged by its intended clinical task and evidence maturity rather than headline accuracy. Multicenter external validation, prospective workflow evaluation, calibration, subgroup reporting, human‐factors testing, and post‐deployment surveillance are prerequisites for safe and equitable clinical use.
Related
- Bridging the Artificial Intelligence Translation Gap in Perioperative Pulmonary and Respiratory Complications: A Practical Framework for Institution-Level Implementation
- Clinical translation of artificial intelligence in musculoskeletal care: a systematic review of current applications, evidence gaps, and implementation readiness
- Harnessing Artificial Intelligence and ChatGPT in Early Detection of Neurodegenerative Disorders: A Transformative Opportunity for Neurology
- The inflection point: embracing responsible artificial intelligence in medical education
- Balancing innovation and ethics: responsible governance of artificial intelligence hallucinations in healthcare.
BibTeX
@article{zahran2026artificial,
title = {{Artificial Intelligence in Neurology: A Critical Narrative Review of Clinical Evidence, Translation, and Responsible Implementation}},
author = {Anwar Zahran and Raya Fuqha and Aya Milhem and Orabi Hajjeh and Hossam Salameh and Mohammed AbuBaha and Waseem Shehadeh and Hammam Jallad and Mohammad Bdair and Issa Snoubar and Omar Abu‐Khazneh and Bara AbuBaha and Ameer Awashra and Aseel Eid and Ahmad Mohammad and Ahmad R. Awwad},
journal = {Brain and Behavior},
year = {2026},
month = nov,
doi = {10.1002/brb3.71873},
url = {https://doi.org/10.1002/brb3.71873},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Artificial Intelligence in Neurology: A Critical Narrative Review of Clinical Evidence, Translation, and Responsible Implementation
AU - Anwar Zahran
AU - Raya Fuqha
AU - Aya Milhem
AU - Orabi Hajjeh
AU - Hossam Salameh
AU - Mohammed AbuBaha
AU - Waseem Shehadeh
AU - Hammam Jallad
AU - Mohammad Bdair
AU - Issa Snoubar
AU - Omar Abu‐Khazneh
AU - Bara AbuBaha
AU - Ameer Awashra
AU - Aseel Eid
AU - Ahmad Mohammad
AU - Ahmad R. Awwad
PY - 2026
DA - 2026/11/06
JO - Brain and Behavior
DO - 10.1002/brb3.71873
UR - https://doi.org/10.1002/brb3.71873
AB - Purpose: Artificial intelligence (AI) is increasingly applied to neuroimaging, electrophysiology, clinical text, and digitally measured behavior, but reported performance often exceeds the maturity of clinical evidence. Method: We conducted a structured narrative review, informed by SANRA principles, of English‐language human studies, reviews, and regulatory sources identified in PubMed/MEDLINE and IEEE Xplore from January 2022 through July 2026; older landmark studies were retained when necessary for historical or methodological context. We applied a common translational‐evidence framework across six prespecified neurological domains to distinguish technical performance from external validation, prospective clinical evaluation, implementation, and demonstrated patient benefit. Evidence was appraised according to cohort representativeness, patient‐level data separation, external and prospective validation, calibration, clinical comparators, regulatory status, workflow integration, and patient‐level benefit. Finding: Across stroke, dementia, Parkinson disease, epilepsy, multiple sclerosis, and neuro‐oncology, the strongest evidence concerns narrowly defined imaging and signal‐processing tasks. A cluster‐randomized stroke study demonstrated shorter thrombectomy workflow time, and several externally validated models show improved portability. In contrast, many high‐accuracy classification and segmentation studies remain retrospective, single‐dataset, internally validated, and uncalibrated. Multimodal learning, foundation models, federated learning, and large language models broaden capability but introduce additional risks related to dataset shift, bias, opacity, privacy, and automation. Conclusion: Neuro‐AI should be judged by its intended clinical task and evidence maturity rather than headline accuracy. Multicenter external validation, prospective workflow evaluation, calibration, subgroup reporting, human‐factors testing, and post‐deployment surveillance are prerequisites for safe and equitable clinical use.
SN - 2162-3279
ER -