AI Prompt Engineering for Neurologists and Trainees

Abstract

Large language models (LLMs) have transformative potential in neurology, impacting clinical decision-making, medical training, and research. Prompt engineering, the strategic design of inputs to optimize LLM performance, is essential for neurologists and trainees seeking to effectively integrate these powerful tools into practice. Carefully crafted prompts enable LLMs to summarize complex patient narratives, generate differential diagnoses, and support patient education. In training, structured prompts enhance diagnostic reasoning, board preparation, and interactive case-based learning. Neurological research also benefits, with LLMs aiding in data extraction, computed phenotype generation, and literature synthesis. Despite their promise, challenges remain, including hallucinations, data bias, privacy concerns, and regulatory complexities. This review synthesizes current advances and highlights best practices, including two structured prompt engineering frameworks tailored to neurology: Role-Task-Format (RTF) for routine use and our newly developed BRAIN (Background, Role, Aim, Instructions, Next steps) for complex tasks. We offer practical guidance to maximize accuracy, safety, and equity in LLM outputs, ensuring reliable support for neurologists and trainees.

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