Large language models ( #LLMs ) have emerged as transformative healthcare tools for clinical documentation, diagnostic reasoning, and medical education. However, effective utilization requires understanding prompt engineering principles—the strategic design of inputs to optimize performance while mitigating hallucination, bias, and outdated information.
Methods
This narrative review synthesizes evidence from a structured PubMed search through December 2025 using terms including “large language models,” “prompt engineering,” “clinical decision support,” and “retrieval-augmented generation.” We included peer-reviewed studies and systematic reviews from 2023 onwards, supplemented by manufacturer benchmarks (acknowledged as non-peer-reviewed), informed by a Belgian Society of Internal Medicine symposium (December 2025).
Results
Effective clinical prompt engineering requires four elements: role definition, context provision, task formulation, and output specification. Structured frameworks—RTF (Role–Task–Format) for routine tasks and BRAIN (Background, Role, Aim, Instructions, Next steps) for complex scenarios—provide systematic approaches. Advanced techniques including chain-of-thought reasoning, few-shot prompting, and retrieval-augmented generation (RAG) enhance performance. RAG improves accuracy by 9.8–16.3% while reducing hallucinations by 11.8–18%. A meta-analysis of 36 studies found pooled LLM accuracy of 72% on medical licensing examinations.
Critical limitations persist: studies demonstrate an inverse correlation between model confidence and accuracy, with lower-performing models paradoxically exhibiting higher confidence. Independent clinical validation remains limited.
Conclusions
LLMs offer efficiency gains while requiring careful implementation. Physicians must develop prompt engineering competency, combining structured frameworks, RAG grounding strategies, and persistent human oversight for safe clinical integration. Prompt engineering literacy represents an essential emerging clinical skill.
https://www.tandfonline.com/doi/full/10.1080/17843286.2026.2613903
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