This paper is in the following e-collection/theme issue:

Digital Health Reviews (1534) Artificial Intelligence (1755) Generative Language Models Including ChatGPT (507) AI Language Models in Health Care (106) Natural Language Processing (804) Artificial Intelligence, Machine Learning, and Natural Language Processing for Public Health (59) Theme Issue: ChatGPT and Generative Language Models in Medical Education (102)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/60501, first published .
Prompt Engineering Paradigms for Medical Applications: Scoping Review

Prompt Engineering Paradigms for Medical Applications: Scoping Review

Prompt Engineering Paradigms for Medical Applications: Scoping Review

Authors of this article:

Jamil Zaghir1, 2 Author Orcid Image ;   Marco Naguib3 Author Orcid Image ;   Mina Bjelogrlic1, 2 Author Orcid Image ;   Aurélie Névéol3 Author Orcid Image ;   Xavier Tannier4 Author Orcid Image ;   Christian Lovis1, 2 Author Orcid Image
Article Authors Cited by (16) Tweetations (1) Metrics Author Video

Journals

  1. Zaghir J, Bjelogrlic M, Goldman J, Ehrsam J, Gaudet-Blavignac C, Lovis C, Hassani H. Human-machine interactions with clinical phrase prediction system, aligning with Zipf’s least effort principle?. PLOS ONE 2024;19(12):e0316177 View
  2. Yuan H. Natural Language Processing for Chest X‐Ray Reports in the Transformer Era: BERT‐Like Encoders for Comprehension and GPT‐Like Decoders for Generation. iRADIOLOGY 2025 View
  3. Azimi I, Qi M, Wang L, Rahmani A, Li Y. Evaluation of LLMs accuracy and consistency in the registered dietitian exam through prompt engineering and knowledge retrieval. Scientific Reports 2025;15(1) View
  4. Turner J. Intuitive Human–Artificial Intelligence Theranostic Complementarity. Cancer Biotherapy and Radiopharmaceuticals 2025;40(3):153 View
  5. Monzon N, Hays F. Leveraging Generative Artificial Intelligence to Improve Motivation and Retrieval in Higher Education Learners. JMIR Medical Education 2025;11:e59210 View
  6. Colangelo M, Guizzardi S, Meleti M, Calciolari E, Galli C. How to Write Effective Prompts for Screening Biomedical Literature Using Large Language Models. BioMedInformatics 2025;5(1):15 View
  7. Hao J, Chen Z, Peng Q, Zhao L, Zhao W, Cong S, Li J, Li J, Qian Q, Sun H. Prompt Framework for Extracting Scale-Related Knowledge Entities from Chinese Medical Literature: Development and Evaluation Study. Journal of Medical Internet Research 2025;27:e67033 View
  8. Tung C, Liang S, Chang S, Young C. A Hybrid Artificial Intelligence System for Automated EEG Background Analysis and Report Generation. IEEE Journal of Biomedical and Health Informatics 2025;29(4):2629 View
  9. Qiu Q, Ma Y, Han P, Ma K, Huang Z, Tian M, Wu Q. Domain knowledge-guided geological named entities recognition of rock minerals based on prompt engineering with error feedback mechanism. Computers & Geosciences 2025;201:105944 View
  10. Vorensky M, Peredo D, Ferraro R, Paris E, Mohammadi A, Spano P, Rao S. Improving ChatGPT’s Performance in Orthopedics: Opportunities Using the CRISPE Framework. JOSPT Methods 2025;1(2):1 View
  11. Galli C, Gavrilova A, Calciolari E. Large Language Models in Systematic Review Screening: Opportunities, Challenges, and Methodological Considerations. Information 2025;16(5):378 View
  12. Shreibati J. Prompting Introspection. Circulation 2025;151(19):1375 View
  13. Abrantes J. Assessing Large Language Models for Medical Question Answering in Portuguese: Open-Source Versus Closed-Source Approaches. Cureus 2025 View
  14. Wang K, Lin L, Zheng R, Nan S, Lu X, Duan H. Leveraging large language models for preoperative prevention of cardiopulmonary bypass-associated acute kidney injury. Renal Failure 2025;47(1) View

Conference Proceedings

  1. Castagnari E, Muyama L, Coulet A. 2024 2nd International Conference on Foundation and Large Language Models (FLLM). Prompting Large Language Models for Supporting the Differential Diagnosis of Anemia View
  2. Mondal C, Pham D, Gupta A, Tan T, Gedeon T. Companion Proceedings of the ACM on Web Conference 2025. Leveraging Prompt Engineering with Lightweight Large Language Models to Label and Extract Clinical Information from Radiology Reports View