Work

Prompt engineering for academic and clinical writing: A practical guide for clinicians

Year: 2026

Type: article

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.

Source: Saudi Journal of Anaesthesia

DOI: 10.4103/sja.sja_501_26

Authors: Alessandro De Cassai, Elisa Pistollato, Tommaso Pettenuzzo, Burhan Dost, Nicolò Sella, Annalisa Boscolo

Institutions: University of Padua, Azienda Ospedale - Università Padova, Ondokuz Mayıs University

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: diamond

Funder: Università degli Studi di Padova