Large language models for neurology: a mini review.

Area of Science:

• Neurology
• Artificial Intelligence
• Medical Informatics

Background:

• Large language models (LLMs) offer transformative potential in healthcare, particularly in neurology.
• Applications span diagnostic reasoning, documentation, and workflow optimization.
• Specific neurological conditions like Alzheimer's disease, Parkinson's disease, multiple sclerosis, and epilepsy are key areas of focus.

Purpose of the Study:

• To review emerging applications of LLMs in neurology.
• To highlight key areas such as ambient documentation, multimodal data integration, and clinical decision support.
• To identify barriers and future directions for LLM adoption in neurological practice.

Main Methods:

• This is a Mini Review, synthesizing current literature and expert perspectives.
• Focus on applications in Alzheimer's disease, Parkinson's disease, multiple sclerosis, and epilepsy.
• Emphasis on ambient documentation, multimodal data integration, and clinical decision support.

Main Results:

• LLMs can augment diagnostic reasoning and streamline clinical documentation.
• Ambient documentation and multimodal data integration are promising applications.
• Key barriers include bias, privacy concerns, reliability issues, and regulatory hurdles.

Conclusions:

• Neurology-focused LLMs require improved fluency in biomedical ontologies and FHIR standards for better interoperability.
• Future impactful developments include integrating multi-omic/neuroimaging data with digital twins for precision neurology and wider adoption of ambient documentation to reduce burden.
• Clinical success hinges on model robustness, ethical governance, and careful implementation.