AI Prompt Engineering for Neurologists and Trainees.

Area of Science:

• Neurology
• Artificial Intelligence
• Medical Informatics

Background:

• Large language models (LLMs) are increasingly influential in healthcare.
• Prompt engineering is crucial for optimizing LLM utility in clinical settings.
• Neurologists and trainees require guidance for effective LLM integration.

Purpose of the Study:

• To review the current applications of LLMs in neurology.
• To highlight best practices in prompt engineering for neurological contexts.
• To introduce structured frameworks for LLM use in neurology.

Main Methods:

• Literature synthesis of LLM applications in neurology.
• Development and presentation of prompt engineering frameworks (RTF and BRAIN).
• Discussion of challenges and practical guidance for LLM implementation.

Main Results:

• LLMs can enhance clinical decision-making, medical training, and neurological research.
• Structured prompts improve LLM performance in summarizing cases, generating diagnoses, and aiding education.
• Two frameworks, RTF and BRAIN, are proposed for different neurological tasks.

Conclusions:

• LLMs hold transformative potential for neurology, but challenges like bias and privacy must be addressed.
• Effective prompt engineering is vital for maximizing LLM accuracy, safety, and equity in neurological applications.
• The RTF and BRAIN frameworks provide practical guidance for neurologists and trainees using LLMs.