Prompt engineering for academic and clinical writing: A practical guide for clinicians.
4 affiliations
- 1Department of Medicine (DIMED), University of Padua, Padua, Veneto, Italy.
- 2Institute of Anesthesia and Intensive Care, University Hospital of Padua, Padua, Veneto, Italy.
- 3Department of Neuroscience (DNS), University of Padua, Padua, Veneto, Italy.
- 4Department of Anesthesiology and Reanimation, Ondokuz Mayis University Faculty of Medicine, Atakum, Samsun, Türkiye.
Abstract
Large language models (LLMs) are increasingly integrated into clinical workflows, offering potential benefits ranging from literature synthesis and clinical decision support to patient education and documentation assistance. However, the quality of LLM output is highly dependent on the quality of the instructions provided, a process known as prompt engineering. This article introduces clinicians to the foundational principles and advanced techniques of prompt engineering, enabling clinicians to extract accurate, contextually relevant, and clinically safe responses. Key strategies discussed include role prompting, chain-of-thought reasoning, few-shot learning, and output constraint specification. The paper also addresses domain-specific considerations, including hallucination risk, the importance of clinical verification, and ethical obligations when using artificial intelligence-generated content in patient care contexts.
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BibTeX
@article{cassai2026prompt,
title = {{Prompt engineering for academic and clinical writing: A practical guide for clinicians.}},
author = {Alessandro De Cassai and Elisa Pistollato and Tommaso Pettenuzzo and Burhan Dost and Nicolò Sella and Annalisa Boscolo},
journal = {Saudi journal of anaesthesia},
year = {2026},
month = oct,
doi = {10.4103/sja.sja_501_26},
url = {https://europepmc.org/article/MED/42960347},
note = {CC BY-NC-SA},
}
RIS
TY - JOUR
TI - Prompt engineering for academic and clinical writing: A practical guide for clinicians.
AU - Alessandro De Cassai
AU - Elisa Pistollato
AU - Tommaso Pettenuzzo
AU - Burhan Dost
AU - Nicolò Sella
AU - Annalisa Boscolo
PY - 2026
DA - 2026/10/01
JO - Saudi journal of anaesthesia
DO - 10.4103/sja.sja_501_26
UR - https://europepmc.org/article/MED/42960347
AB - Large language models (LLMs) are increasingly integrated into clinical workflows, offering potential benefits ranging from literature synthesis and clinical decision support to patient education and documentation assistance. However, the quality of LLM output is highly dependent on the quality of the instructions provided, a process known as prompt engineering. This article introduces clinicians to the foundational principles and advanced techniques of prompt engineering, enabling clinicians to extract accurate, contextually relevant, and clinically safe responses. Key strategies discussed include role prompting, chain-of-thought reasoning, few-shot learning, and output constraint specification. The paper also addresses domain-specific considerations, including hallucination risk, the importance of clinical verification, and ethical obligations when using artificial intelligence-generated content in patient care contexts.
SN - 1658-354X
SN - 0975-3125
ER -