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

AI Language Models in Health Care (286) Clinical Information and Decision Making (2141) Electronic Health Records (1415) Ontologies, Classifications, and Coding (358) Artificial Intelligence (2446) Vulnerable Populations in Health Research (275) Generative Language Models Including ChatGPT (880) Language Processing, Classifiers, and Syndrome Definitions (47)

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 (2) 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
  2. Liu T, Qi X, Guo M, Ye X, Fan L, Yang D. Performance evaluation of five large language models for assisting in the interpretation of urinalysis reports for kidney diseases: a real-world study. Clinical Chemistry and Laboratory Medicine (CCLM) 2026 View