Prompting for Healthcare Professionals: Enhancing Clinical Decision-Making with Artificial Intelligence
Introduction. Generative Artificial Intelligence (AI), specifically through Large Language Models (LLMs), is progressively reshaping clinical documentation, decision support, patient education, and research synthesis in healthcare. Despite significant benefits, these models pose challenges such as...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Firenze University Press
2025-03-01
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| Series: | Infermieristica Journal |
| Subjects: | |
| Online Access: | https://riviste.fupress.net/index.php/if/article/view/3198 |
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| Summary: | Introduction. Generative Artificial Intelligence (AI), specifically through Large Language Models (LLMs), is progressively reshaping clinical documentation, decision support, patient education, and research synthesis in healthcare. Despite significant benefits, these models pose challenges such as inaccuracies (hallucinations) and inherent biases. This paper highlights prompt engineering as an emerging and critical skill for healthcare professionals and demonstrates how structured prompting techniques can improve the reliability, clinical relevance, and ethical compliance of AI-driven applications.
Methods. A systematic review of recent literature was conducted to present structured prompt engineering methodologies specifically tailored to healthcare settings. Advanced prompting techniques, including chain-of-thought reasoning, zero-shot and few-shot prompting, and self-consistency strategies, were examined.
Results. The proposed structured approach encompasses clear objective definition, precise contextualization, integration of domain-specific knowledge, iterative refinement, and ethical risk mitigation. Practical guidelines are provided for designing prompts suitable for clinical scenarios, such as diagnostic decisions, patient-specific therapeutic protocols, and administrative tasks. Notably, advanced techniques such as chain-of-thought reasoning and self-consistency effectively reduce inaccuracies and enhance clinical decision-making.
Discussion and Conclusion. The structured integration of prompt engineering optimizes clinical decision-making and supports adherence to evidence-based practices. Incorporating prompt engineering into healthcare educational programs and fostering interdisciplinary collaboration are crucial for the responsible implementation of generative AI. These advancements have far-reaching implications for improving clinical effectiveness, enhancing patient care quality, and elevating the standards of healthcare education and professional practice.
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| ISSN: | 2785-7018 |