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

Development and Evaluation of Research Methods, Instruments and Tools (439) Generative Language Models Including ChatGPT (218) Clinical Informatics (919) Ontologies, Classifications, and Coding (227) Electronic Health Records (971) Artificial Intelligence (1216) Decision Support for Health Professionals (1147) Natural Language Processing (650)

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 (1) Tweetations (2) Metrics

Lei Wang   1 , MS ;   Wenshuai Bi   1 , MS ;   Suling Zhao   1 , MS ;   Yinyao Ma   2 , MS, MD ;   Longting Lv   1 , MS ;   Chenwei Meng   1 , MS ;   Jingru Fu   1 , MS ;   Hanlin Lv   1 , PhD, MD

1 BGI Research, Shenzhen, China

2 The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi, China

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