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Cited by 709Artificial Intelligence in Healthcare and EducationTopic ModelingArtificial Intelligence Applications

Prompt Engineering as an Important Emerging Skill for Medical Professionals: Tutorial

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Meskó, Bertalan·2023·Journal of Medical Internet Research
DOI 10.2196/50638PMC PMC10585440

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Abstract

Prompt engineering is a relatively new field of research that refers to the practice of designing, refining, and implementing prompts or instructions that guide the output of large language models (LLMs) to help in various tasks. With the emergence of LLMs, the most popular one being ChatGPT that has attracted the attention of over a 100 million users in only 2 months, artificial intelligence (AI), especially generative AI, has become accessible for the masses. This is an unprecedented paradigm shift not only because of the use of AI becoming more widespread but also due to the possible implications of LLMs in health care. As more patients and medical professionals use AI-based tools, LLMs being the most popular representatives of that group, it seems inevitable to address the challenge to improve this skill. This paper summarizes the current state of research about prompt engineering and, at the same time, aims at providing practical recommendations for the wide range of health care professionals to improve their interactions with LLMs.

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  • Prompt engineering has emerged as an effective, low-cost method of enhancing the quality of large language model outputs for specific tasks.

    “Alongside recent advancements in LLMs, prompt engineering has emerged as an effective, low-cost method of enhancing the quality of LLM outputs for specific tasks.”

    From A pilot feasibility study comparing large language models in extracting key information from ICU patient text records from an Irish population (2024) · cites this paper as [23]

    The sections “The Emergence of Large Language Models and Prompt Engineering” and “Specific Recommendations for Better LLM Prompts” describe using prompts to improve LLM outputs for particular tasks. The paper does not establish that prompt engineering is low-cost; its mention of decreasing costs concerns LLMs’ contributions to health care more generally. It also presents prompt engineering’s benefits largely as potential or practical guidance, rather than demonstrated effectiveness. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • LLMs have in-context learning capabilities that allow them to adapt to the given prompt.

    “LLMs have in-context learning capabilities that allow them to adapt to the given prompt, so prompts can be intentionally designed to support performance on further tasks, such as patient communication, administrative tasks, risk assessments, or clinical decision-making.160,161”

    From Large Language Models for Chatbot Health Advice Studies: A Systematic Review (2025) · cites this paper as [160]

  • Prompts can be intentionally designed to support performance on further tasks, such as patient communication, administrative tasks, risk assessments, or clinical decision-making.

    “LLMs have in-context learning capabilities that allow them to adapt to the given prompt, so prompts can be intentionally designed to support performance on further tasks, such as patient communication, administrative tasks, risk assessments, or clinical decision-making.160,161”

    From Large Language Models for Chatbot Health Advice Studies: A Systematic Review (2025) · cites this paper as [160]

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Research works printed but not linked: 6

  1. [2]Mesko B The ChatGPT (generative artificial intelligence) revolution has made artificial intelligence approachable for medical professionals J Med Internet Res 2023 06 22 25 e48392 10.2196/48392 37347508 v25i1e48392 37347508 PMC10337400 · DOI
  2. [7]Meskó Bertalan Topol EJ The imperative for regulatory oversight of large language models (or generative AI) in healthcare NPJ Digit Med 2023 07 06 6 1 120 10.1038/s41746-023-00873-0 10.1038/s41746-023-00873-0 37414860 10.1038/s41746-023-00873-0 37414860 PMC10326069 · DOI · Paper page
  3. [9]Wang J Shi E Yu S Wu Z Ma C Dai H Yang Q Kang Y Wu J Hu H Yue C Prompt engineering for healthcare: Methodologies and applications arXiv. Preprint posted online April 28, 2023
  4. [10]Giray L Prompt engineering with ChatGPT: a guide for academic writers Ann Biomed Eng 2023 06 07 10.1007/s10439-023-03272-4 37284994 10.1007/s10439-023-03272-4 37284994 · DOI · Paper page
  5. [11]Lim S Schmälzle R Artificial intelligence for health message generation: an empirical study using a large language model (LLM) and prompt engineering Front Commun 2023 5 26 8 10.3389/fcomm.2023.1129082 · DOI
  6. [12]Meskó Bertalan Görög Marton A short guide for medical professionals in the era of artificial intelligence NPJ Digit Med 2020 09 24 3 1 126 10.1038/s41746-020-00333-z 10.1038/s41746-020-00333-z 33043150 10.1038/s41746-020-00333-z 33043150 PMC7518439 · DOI · Paper page

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Authors
Meskó, B.
Corresponding author
Meskó, Bertalan
ORCID
Meskó, Bertalan
Venue
Journal of Medical Internet Research
Publisher
JMIR Publications Inc.
Published
2023-10-04
Volume
25
Pages
e50638
ISSN
1438-8871 (ISSN-L)
DOI
10.2196/50638
PMC
PMC10585440
OpenAlex
W4386867830
Cited by
709
References
1
Citations by year
2026: 183 · 2025: 308 · 2024: 208 · 2023: 10
FWCI
28.31
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Journal of Medical Internet Research (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords
prompt engineering, large language models, ChatGPT, generative AI, medical professionals, artificial intelligence in healthcare, human-AI interaction
MeSH terms
Artificial Intelligence, Engineering, Health Personnel, Humans, Language

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