# Large language models for neurology: a mini review

## TL;DR

This paper reviews how large language models can improve neurology by aiding diagnosis, documentation, and workflow, while addressing challenges like bias and privacy.

## Contribution

The paper highlights novel applications of large language models in neurology and outlines future directions for precision neurology and clinical integration.

## Key findings

- Large language models can enhance diagnostic reasoning and clinical workflows in neurology.
- Ambient documentation and multimodal data integration are promising applications.
- Challenges include bias, privacy, and regulatory alignment.

## Abstract

Large language models have the potential to transform neurology by augmenting diagnostic reasoning, streamlining documentation, and improving workflow efficiency. This Mini Review surveys emerging applications of large language models in Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, and epilepsy, with emphasis on ambient documentation, multimodal data integration, and clinical decision support. Key barriers to adoption include bias, privacy, reliability, and regulatory alignment. Looking ahead, neurology-focused language models may develop greater fluency in biomedical ontologies and FHIR standards, improving data interoperability and supporting more seamless collaboration between clinicians and AI systems. Two future developments have the potential to be particularly impactful: (1) the integration of multi-omic and neuroimaging data with digital-twin simulations to advance precision neurology, and (2) broader adoption of ambient documentation and other language-model–based efficiencies that could reduce administrative and cognitive burden. Ultimately, the clinical success of large language models will depend on continued progress in model robustness, ethical governance, and careful implementation.