Work

Artificial Intelligence in Neurology: A Critical Narrative Review of Clinical Evidence, Translation, and Responsible Implementation

Year: 2026

Type: review

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.

Source: Brain and Behavior

DOI: 10.1002/brb3.71873