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    Structured prompting significantly improves large language model (LLM) performance for acute stroke thrombolysis clinical decision support. This approach enhances safety and guideline adherence, crucial for deploying LLMs in clinical settings.

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    Area of Science:

    • Artificial Intelligence in Medicine
    • Clinical Decision Support Systems
    • Neurology
    • Natural Language Processing

    Background:

    • Large language models (LLMs) show promise for clinical decision support but exhibit variable accuracy.
    • Prompt engineering can optimize LLM performance in clinical settings, yet specific best practices for neurology remain underexplored.
    • Effective LLM integration requires understanding how to best elicit accurate and safe clinical recommendations.

    Purpose of the Study:

    • To compare the impact of structured prompting (CARDS) versus naive prompting on LLM performance for acute stroke thrombolysis clinical decision support (CDS).
    • To evaluate four LLMs (two closed-source and two open-source) using realistic clinical vignettes.
    • To assess performance across key domains including guideline adherence, safety, and clarity of recommendations.

    Main Methods:

    • Three novel ischemic stroke vignettes were presented to LLMs using either a naive question or a five-step structured prompt (CARDS).
    • The CARDS prompt guided information extraction, timing analysis, contraindication checking, decision explanation, and risk-benefit discussion.
    • LLM outputs were evaluated on guideline adherence, unsafe recommendations, risk recognition, guideline grading accuracy, conversational explanation, clarity, and helpfulness.

    Main Results:

    • Structured prompting significantly improved performance across most evaluated domains for both closed-source (GPT-4o, o3) and open-source (r1-1776) models.
    • Closed-source models and the r1-1776 model achieved 100% guideline adherence and eliminated unsafe recommendations with structured prompts.
    • Other open-source models showed modest gains, highlighting variability in LLM responses to structured prompting.

    Conclusions:

    • Structured prompting substantially enhances LLM performance for acute stroke thrombolysis clinical decision support, improving safety and adherence.
    • Specific models, including proprietary and open-source options, demonstrated excellent performance with structured prompts.
    • Structured prompts are essential for clinical LLM deployment, though human oversight remains critical.