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

Development and Evaluation of Research Methods, Instruments and Tools (632) Generative Language Models Including ChatGPT (497) Clinical Informatics (1162) Ontologies, Classifications, and Coding (271) Electronic Health Records (1183) Artificial Intelligence (1740) Decision Support for Health Professionals (1338) Natural Language Processing (801)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/53216, first published .
Investigating the Impact of Prompt Engineering on the Performance of Large Language Models for Standardizing Obstetric Diagnosis Text: Comparative Study

Investigating the Impact of Prompt Engineering on the Performance of Large Language Models for Standardizing Obstetric Diagnosis Text: Comparative Study

Investigating the Impact of Prompt Engineering on the Performance of Large Language Models for Standardizing Obstetric Diagnosis Text: Comparative Study

Authors of this article:

Lei Wang1 Author Orcid Image ;   Wenshuai Bi1 Author Orcid Image ;   Suling Zhao1 Author Orcid Image ;   Yinyao Ma2 Author Orcid Image ;   Longting Lv1 Author Orcid Image ;   Chenwei Meng1 Author Orcid Image ;   Jingru Fu1 Author Orcid Image ;   Hanlin Lv1 Author Orcid Image
Article Authors Cited by (9) Tweetations (2) Metrics

Journals

  1. Dos Santos F, Johnson L, Madandola O, Priola K, Yao Y, Macieira T, Keenan G. An example of leveraging AI for documentation: ChatGPT-generated nursing care plan for an older adult with lung cancer. Journal of the American Medical Informatics Association 2024;31(9):2089 View
  2. Desseauve D, Lescar R, de la Fourniere B, Ceccaldi P, Dziadzko M. AI in obstetrics: Evaluating residents’ capabilities and interaction strategies with ChatGPT. European Journal of Obstetrics & Gynecology and Reproductive Biology 2024;302:238 View
  3. Chakraborty C, Bhattacharya M, Pal S, Lee S. Prompt engineering-enabled LLM or MLLM and instigative bioinformatics pave the way to identify and characterize the significant SARS-CoV-2 antibody escape mutations. International Journal of Biological Macromolecules 2025;287:138547 View
  4. Guo L, Zuo Y, Yisha Z, Liu J, Gu A, Yushan R, Liu G, Li S, Liu T, Wang X. Diagnostic performance of advanced large language models in cystoscopy: evidence from a retrospective study and clinical cases. BMC Urology 2025;25(1) View
  5. Chen Q, Hu Y, Peng X, Xie Q, Jin Q, Gilson A, Singer M, Ai X, Lai P, Wang Z, Keloth V, Raja K, Huang J, He H, Lin F, Du J, Zhang R, Zheng W, Adelman R, Lu Z, Xu H. Benchmarking large language models for biomedical natural language processing applications and recommendations. Nature Communications 2025;16(1) View

Books/Policy Documents

  1. Paraschiv E, Elena Cîrnu C, Victor Vevera A. Electronic Health Records - Issues and Challenges in Healthcare Systems [Working Title]. View
  2. Huang C, Rianto B, Sun J, Fu Z, Lee C. Large Language Models for Automatic Deidentification of Electronic Health Record Notes. View
  3. Marcu S, Mi Y, Tangney M, Tabirca S. Modelling and Development of Intelligent Systems. View

Conference Proceedings

  1. Wei Q, Chen X, Ni Y, Cao C. Proceedings of the 2024 International Conference on Artificial Intelligence and Teacher Education. A Technical Framework for Recognizing and Interpreting Complex Medical Records: Based on Multimodal Large Language Model View