Large Language Models in Clinical Neurology: A Systematic Review
Abstract
BACKGROUND: Large language models (LLMs) are increasingly explored for clinical applications in neurology, yet their real-world utility, safety, and optimal implementation remain uncertain. We systematically reviewed the literature to characterize current applications, evaluate evidence quality, and identify knowledge gaps regarding LLM use in clinical neurology.
METHODS: Following PRISMA guidelines, we searched PubMed, Embase, Scopus, Web of Science, and CENTRAL from January 1, 2022 through February 1 2026. for peer-reviewed studies evaluating LLM applications in clinical neurology. We included studies using large language models for clinically relevant neurology tasks from text or multimodal inputs. Two independent reviewers screened records, extracted data, and assessed risk of bias using the QUADS-AI. We synthesized evidence narratively across application domains, validation approaches, and model performance.
RESULTS: Thirty-six studies (published 2023-2026) spanning 8 neurology subspecialties met inclusion criteria; 13 were simulation or feasibility studies, 17 analyzed retrospective clinical data, and 6 reported prospective clinical validation. Proprietary models predominated; 7 studies used retrieval-augmented generation (RAG) and 3 used agentic frameworks. Performance was highest for constrained tasks, including binary diagnostic classification (area under the curve, AUC 0.75-0.94), information extraction (F1 score, 0.89-0.90), patient education question answering…
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Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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