PROMPT ENGINEERING IN MEDICINE: HARASSING THE POTENTIAL OF ARTIFICIAL INTELLIGENCE IN CLINICAL PRACTICE, EDUCATION, AND RESEARCH
Keywords:
Prompt engineering, large language models, clinical decision support, medical education, artificial intelligence in healthcare, evidence-based medicine, digital literacy.Abstract
Large language models (LLMs) are transforming approaches to medical information processing, but the quality of their output critically depends on the structure of the input query. Prompt engineering—a methodology for the purposeful construction of text instructions for LLMs—is becoming an essential tool for physician researchers, clinicians, and medical faculty. This article systematizes the basic principles of prompt engineering, including defining a role, context, output format, and reasoning chain. Specific application scenarios are considered in three domains: scientific research (cohort data analysis, systematic literature review, manuscript editing), medical education (problem-based learning, clinical case and MCQ generation), and clinical practice (decision support, differential diagnosis, preoperative planning). Ethical considerations are discussed, including the risk of hallucinations, data privacy, and the essential role of expert verification. Development prospects are linked to the development of digital literacy as a core competency of medical professionals.
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