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

AI Language Models in Health Care (180) Electronic Health Records (1291) Generative Language Models Including ChatGPT (658) Clinical Information and Decision Making (1860) Artificial Intelligence (2043) Vulnerable Populations in Health Research (222) Language Processing, Classifiers, and Syndrome Definitions (47) Ontologies, Classifications, and Coding (317)

Published on in Vol 13 (2025)

This is a member publication of University of Toronto

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/68955, first published .
Efficient Detection of Stigmatizing Language in Electronic Health Records via In-Context Learning: Comparative Analysis and Validation Study

Efficient Detection of Stigmatizing Language in Electronic Health Records via In-Context Learning: Comparative Analysis and Validation Study

Efficient Detection of Stigmatizing Language in Electronic Health Records via In-Context Learning: Comparative Analysis and Validation Study

Authors of this article:

Hongbo Chen1 Author Orcid Image ;   Myrtede Alfred1 Author Orcid Image ;   Eldan Cohen1 Author Orcid Image
Article Authors Cited by (1) Tweetations Metrics

Journals

  1. Haldar S, Bear Don't Walk IV O, Akter S. From Context to Care: Rethinking Stigma Detection in Clinical Language Models. JMIR Medical Informatics 2025;13:e82484 View