From diagnostics to education: Multi-domain evaluation of LLM chatbots in neurology

TL;DR

This paper reviews how large language models are being used in neurology for tasks like diagnosis and research, highlighting both their potential and the challenges they face.

Contribution

A systematic review of LLM applications in neurology, identifying key areas and challenges.

Findings

LLMs are used in hypothesis generation, clinical decision support, and cognitive modeling in neuroscience.

LLMs can process large datasets and support personalized medicine in neurological care.

Challenges include interpretability, ethical issues, and the need for domain-specific training.

Abstract

The development of large language models (LLMs) has shown promising results in enhancing research processes, data analysis, and communication in various domains of neurology. In this work, we systematically review and synthesize current evidence on the applications of LLMs in the assessment, diagnosis, and monitoring of neurological disorders.

Three databases, namely PubMed, Scopus, and Web of Science, were considered for document search. Article selection was according to PRISMA guidelines, and Newcastle–Ottawa Scale (NOS) was used to assess the article quality based on relevance, quality, and applicability.

Nine studies were included in the final analysis. Based on the findings, LLMs have been utilized in diverse areas of neuroscience including hypothesis generation, clinical decision support, and cognitive modeling. LLMs can process large datasets, identify trends, and support personalized medicine. However, challenges such as interpretability, ethical considerations, and domain-specific training remain critical.

By facilitating workflows and uncovering new insights, LLMs can revolutionize different domains of neurology. Nevertheless, further research on their reliability, ethical implications, and adaptation to the unique demands of neuroscience is needed.

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