A hybrid-reasoner LLM framework toward real-world clinical decision- making support in acute ischemic stroke

Acute ischemic stroke (AIS) is a leading cause of mortality and disability worldwide, with outcomes critically dependent on timely and accurate treatment. Yet prognosis is undermined by uneven expertise, resource shortages, and diagnostic delays. Large language models (LLMs), through rapid and accurate interpretation, may bridge this gap and improve care delivery. Here, we developed a hybrid-reasoner framework that augments LLMs with structured clinical reasoning to reliably support time-critical, guideline-concordant AIS emergency decision-making, particularly the need for timely and accurate care in resource-limited settings. We present the first multicenter, multisource, cross-scenario evaluation encompassing both retrospective and prospective real-world clinical cases, as well as literature-derived cases. Across model scales, framework augmentation yielded consistent and substantial gains in treatment accuracy, with average improvements of 18.9% compared with standalone LLMs. Safety evaluation showed that the augmented DeepSeek-R1-671B achieved low hallucination (10.9%), omission (14.7%), and a high overall safety score (4.36/5). Notably, human–AI interaction experiments revealed that junior and non-specialist physicians benefited most, narrowing expertise gaps. Collectively, these findings demonstrate that hybrid-reasoner augmented LLMs enhance accuracy, safety, and guideline-concordant decision-making in AIS. This study marks the transition from technical optimization to real-world translation, laying the groundwork for lightweight, safe, and equitable integration of LLMs into stroke center networks, telemedicine, and resource-limited settings.