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

Natural Language Processing (706) Clinical Information and Decision Making (1434) Clinical Informatics (1050) Machine Learning (1501) Ontologies, Classifications, and Coding (243) Artificial Intelligence (1421) Tools, Programs and Algorithms (275) Natural Language Processing (24) Chatbots and Conversational Agents (543) Generative Language Models Including ChatGPT (325)

Published on in Vol 12 (2024)

This is a member publication of University of Pittsburgh

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/55318, first published .
An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing: Algorithm Development and Validation Study

An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing: Algorithm Development and Validation Study

An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing: Algorithm Development and Validation Study

Authors of this article:

Sonish Sivarajkumar1 Author Orcid Image ;   Mark Kelley2 Author Orcid Image ;   Alyssa Samolyk-Mazzanti2 Author Orcid Image ;   Shyam Visweswaran1, 3 Author Orcid Image ;   Yanshan Wang1, 2, 3 Author Orcid Image
Article Authors Cited by (15) Tweetations (4) Metrics

Journals

  1. Fang Y, Ryan P, Weng C. Knowledge-guided generative artificial intelligence for automated taxonomy learning from drug labels. Journal of the American Medical Informatics Association 2024;31(9):2065 View
  2. Nwachukwu B, Varady N, Allen A, Dines J, Altchek D, Williams R, Kunze K. Currently Available Large Language Models Do Not Provide Musculoskeletal Treatment Recommendations That Are Concordant With Evidence-Based Clinical Practice Guidelines. Arthroscopy: The Journal of Arthroscopic & Related Surgery 2024 View
  3. Shahriar S, Lund B, Mannuru N, Arshad M, Hayawi K, Bevara R, Mannuru A, Batool L. Putting GPT-4o to the Sword: A Comprehensive Evaluation of Language, Vision, Speech, and Multimodal Proficiency. Applied Sciences 2024;14(17):7782 View
  4. Zaghir J, Naguib M, Bjelogrlic M, Névéol A, Tannier X, Lovis C. Prompt Engineering Paradigms for Medical Applications: Scoping Review. Journal of Medical Internet Research 2024;26:e60501 View
  5. Tong L, Zhang C, Liu R, Yang J, Sun Z. Comparative performance analysis of large language models: ChatGPT-3.5, ChatGPT-4 and Google Gemini in glucocorticoid-induced osteoporosis. Journal of Orthopaedic Surgery and Research 2024;19(1) View
  6. Tam T, Sivarajkumar S, Kapoor S, Stolyar A, Polanska K, McCarthy K, Osterhoudt H, Wu X, Visweswaran S, Fu S, Mathur P, Cacciamani G, Sun C, Peng Y, Wang Y. A framework for human evaluation of large language models in healthcare derived from literature review. npj Digital Medicine 2024;7(1) View
  7. Ronquillo J, Ye J, Gorman D, Lemeshow A, Watt S. Practical Aspects of Using Large Language Models to Screen Abstracts for Cardiovascular Drug Development: Cross-Sectional Study. JMIR Medical Informatics 2024;12:e64143 View
  8. Workman T, Ahmed A, Sheriff H, Raman V, Zhang S, Shao Y, Faselis C, Fonarow G, Zeng-Treitler Q. ChatGPT-4 extraction of heart failure symptoms and signs from electronic health records. Progress in Cardiovascular Diseases 2024;87:44 View
  9. Das M, Senapati A. Co-reference Resolution in Prompt Engineering. Procedia Computer Science 2024;244:194 View
  10. Othman A, Chemnad K, Tlili A, Da T, Wang H, Huang R. Comparative analysis of GPT-4, Gemini, and Ernie as gloss sign language translators in special education. Discover Global Society 2024;2(1) View
  11. Acut D, Malabago N, Malicoban E, Galamiton N, Garcia M. “ChatGPT 4.0 Ghosted Us While Conducting Literature Search:” Modeling the Chatbot’s Generated Non-Existent References Using Regression Analysis. Internet Reference Services Quarterly 2024:1 View
  12. Cardamone N, Olfson M, Schmutte T, Ungar L, Liu T, Cullen S, Williams N, Marcus S. Classifying Unstructured Text in Electronic Health Records for Mental Health Prediction Models using a Large Language Model (Preprint). JMIR Medical Informatics 2024 View

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  1. Miller S, Busby-Earle C. Proceedings of the Future Technologies Conference (FTC) 2024, Volume 4. View