Effective prompt design for large language models in clinical practice.

Area of Science:

• Artificial Intelligence in Medicine
• Clinical Informatics
• Health Information Technology

Background:

• Large language models (LLMs) show promise in clinical documentation, diagnostics, and education.
• Effective LLM utilization hinges on prompt engineering to manage performance, bias, and data accuracy.
• Understanding prompt engineering is vital for leveraging LLMs in healthcare.

Purpose of the Study:

• To review and synthesize evidence on prompt engineering for large language models in clinical settings.
• To identify key principles and advanced techniques for optimizing LLM performance in healthcare.
• To highlight limitations and future directions for safe LLM integration into clinical practice.

Main Methods:

• A narrative review of studies identified via a structured PubMed search up to December 2025.
• Inclusion of peer-reviewed studies and systematic reviews from 2023 onwards.
• Supplementation with manufacturer benchmarks and insights from a medical symposium.

Main Results:

• Effective clinical prompt engineering involves role definition, context, task formulation, and output specification.
• Structured frameworks (RTF, BRAIN) and advanced techniques like retrieval-augmented generation (RAG) improve LLM accuracy and reduce hallucinations.
• LLMs achieved 72% accuracy on medical licensing exams, but model confidence often inversely correlates with accuracy, and clinical validation is limited.

Conclusions:

• Large language models offer significant efficiency gains in healthcare but require careful implementation.
• Physicians need prompt engineering skills, structured frameworks, RAG strategies, and human oversight for safe clinical use.
• Prompt engineering literacy is an essential emerging skill for healthcare professionals.