Generative large language models in the clinical management of Alzheimer's disease and mild cognitive impairment
Abstract
Background: Dementia affects over 55 million people worldwide. Mild cognitive impairment (MCI) often precedes Alzheimer's disease (AD). Clinical management requires integrating uncertain evidence from neuropsychological testing, neuroimaging, and biomarkers. Large language models (LLMs) also generate probabilistic outputs, but whether they can reliably support diagnostic, therapeutic, or educational tasks in AD and MCI has not been systematically examined.
Methods: We searched PubMed, Scopus, and PubMed Central (January 2023 to April 2026) for studies evaluating generative LLMs on clinical tasks in Alzheimer's disease (AD) or mild cognitive impairment (MCI). Risk of bias was assessed using QUADAS-AI and AXIS. Narrative synthesis followed the SWiM guideline.
Prospero: CRD420261372436.
Results: Eleven studies were included: diagnosis (n = 3), treatment guidance (n = 2), and patient/caregiver education (n = 8); two studies contributed to multiple domains. Diagnostic models achieved high internal accuracy (0.94-0.97) but declined on external validation; three-way classification accuracy dropped approximately 7% points, and MMSE-prediction R² collapsed from 0.90 to 0.25 on an external dataset. Treatment guidance approached but did not match structured clinical guidelines. Educational outputs were rated moderate to high quality but lacked source attribution and exceeded recommended reading levels; retrieval augmentation improved usability without improving accuracy. Hallucination…
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Evidence Hub reads this as a systematic review, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid. Reported size: 55 participants or records.
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