Current Landscape and Future Directions Regarding Generative Large Language Models in Stroke Care: Scoping Review

Stroke has a major impact on global health, causing long-term disability and straining health care resources. Generative large language models (gLLMs) have emerged as promising tools to help address these challenges, but their applications and reported performance in stroke care require comprehensive mapping and synthesis.

The aim of this scoping review was to consolidate a fragmented evidence base and examine the current landscape, shortcomings, and future directions in the design, reporting, and evaluation of gLLM-based interventions in stroke care.

In this scoping review, which adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines and the Population, Concept, and Context (PCC) framework, we searched 6 major scientific databases in December 2024 for gLLM-based interventions across the stroke care pathway, mapping their key characteristics and outcomes.

A total of 25 studies met the predefined eligibility criteria and were included for analysis. Retrospective designs predominated (n=16, 64%). Key applications of gLLMs included clinical decision-making support (n=10, 40%), administrative assistance (n=9, 36%), direct patient interaction (n=5, 20%), and automated literature review (n=1, 4%). Implementations mainly used generative pretrained transformer models accessed through task-prompted chat interfaces. In total, 5 key challenges were identified from the included studies during the implementation of gLLM-based interventions: ensuring factual alignment, maintaining system robustness, enhancing interpretability, optimizing efficiency, and facilitating clinical adoption.

The application of gLLMs in stroke care, while promising, remains relatively new, with most interventions reflecting early-stage or relatively simple implementations. Against this backdrop, critical gaps in research and clinical translation persist. To support the development of clinically impactful and trustworthy applications, we propose an actionable framework that prioritizes real-world evidence, mandates transparent technical reporting, broadens evaluation beyond output accuracy, strengthens validation of advanced task adaptation strategies, and investigates mechanisms for safe and effective human-gLLM interaction.