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

Information Retrieval (348) Artificial Intelligence (2129) Electronic Health Records (1319) Chatbots and Conversational Agents (740) Generative Language Models Including ChatGPT (708)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66279, first published .
Using Synthetic Health Care Data to Leverage Large Language Models for Named Entity Recognition: Development and Validation Study

Using Synthetic Health Care Data to Leverage Large Language Models for Named Entity Recognition: Development and Validation Study

Using Synthetic Health Care Data to Leverage Large Language Models for Named Entity Recognition: Development and Validation Study

Authors of this article:

Hendrik Šuvalov1 Author Orcid Image ;   Mihkel Lepson1 Author Orcid Image ;   Veronika Kukk1 Author Orcid Image ;   Maria Malk1 Author Orcid Image ;   Neeme Ilves1 Author Orcid Image ;   Hele-Andra Kuulmets1 Author Orcid Image ;   Raivo Kolde1 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

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  1. Yang H, Li M, Zhou H, Xiao Y, Fang Q, Zhou S, Zhang R. Large Language Model Synergy for Ensemble Learning in Medical Question Answering: Design and Evaluation Study. Journal of Medical Internet Research 2025;27:e70080 View
  2. Ioanovici A, Feier A, Mărușteri M, Trâmbițaș-Miron A, Dobru D. Enhancing and Not Replacing Clinical Expertise: Improving Named-Entity Recognition in Colonoscopy Reports Through Mixed Real–Synthetic Training Sources. Journal of Personalized Medicine 2025;15(8):334 View
  3. Kim Y, Kim C, Kim Y. One Report, Multifaceted Views: Multi-Expert Rewriting for ECG Interpretation. Applied Sciences 2025;15(17):9376 View