{
  "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.\n\nMethod: 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.\n\nFinding: 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.\n\nConclusion: 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.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Medicine Faculty of Medicine and Health Sciences An‐Najah National University Nablus Palestine",
    "Hurley Medical Center‐MSU Flint Michigan USA",
    "Department of Neurology Mercy St. Vincent Medical Center Toledo Ohio USA"
  ],
  "article_type": "review article",
  "arxiv": null,
  "author_total": 16,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Anwar Zahran"
    },
    {
      "affs": [
        0
      ],
      "name": "Raya Fuqha"
    },
    {
      "affs": [
        0
      ],
      "name": "Aya Milhem"
    },
    {
      "affs": [
        0
      ],
      "name": "Orabi Hajjeh"
    },
    {
      "affs": [
        0
      ],
      "name": "Hossam Salameh"
    },
    {
      "affs": [
        0
      ],
      "name": "Mohammed AbuBaha"
    },
    {
      "affs": [
        0
      ],
      "name": "Waseem Shehadeh"
    },
    {
      "affs": [
        0
      ],
      "name": "Hammam Jallad"
    },
    {
      "affs": [
        0
      ],
      "name": "Mohammad Bdair"
    },
    {
      "affs": [
        0
      ],
      "name": "Issa Snoubar"
    },
    {
      "affs": [
        0
      ],
      "name": "Omar Abu‐Khazneh"
    },
    {
      "affs": [
        0
      ],
      "name": "Bara AbuBaha"
    },
    {
      "affs": [
        0
      ],
      "name": "Ameer Awashra"
    },
    {
      "affs": [
        0
      ],
      "name": "Aseel Eid"
    },
    {
      "affs": [
        1
      ],
      "name": "Ahmad Mohammad"
    },
    {
      "affs": [
        2
      ],
      "name": "Ahmad R. Awwad"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ckwp0vz82hyqj.bib",
  "cited_by": 0,
  "doi": "10.1002/brb3.71873",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13741994/",
  "id": "ckwp0vz82hyqj",
  "issns": [
    "2162-3279"
  ],
  "landing_url": "https://doi.org/10.1002/brb3.71873",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Review"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7225411820",
  "pdf_url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/brb3.71873",
  "pmcid": "PMC13741994",
  "pmid": "42992946",
  "published": "2026-11-06",
  "publisher": "Wiley",
  "ris": "https://research.draconitic.com/paper/ckwp0vz82hyqj.ris",
  "sources": [
    {
      "first_seen": "2026-11-10T12:22:55Z",
      "native_id": "PMC13741994",
      "source": "pmc",
      "url": "https://doi.org/10.1002/brb3.71873"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Artificial Intelligence in Neurology: A Critical Narrative Review of Clinical Evidence, Translation, and Responsible Implementation",
  "title_text": "Artificial Intelligence in Neurology: A Critical Narrative Review of Clinical Evidence, Translation, and Responsible Implementation",
  "type": "peer-reviewed",
  "updated_at": "2026-11-10T12:22:55Z",
  "url": "https://research.draconitic.com/paper/ckwp0vz82hyqj",
  "venue": "Brain and Behavior",
  "versions": []
}