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

Artificial Intelligence (AI) in Medical Education (235) New Methods and Approaches in Medical Education (426) Graduate and Postgraduate Education for Health Professionals (146) Testing and Assessment in Medical Education (116) Specialist Training for Doctors (66)

Published on in Vol 11 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/82326, first published .
Quantifying Emergency Medicine Residency Learning Curves Using Natural Language Processing: Retrospective Cohort Study

Quantifying Emergency Medicine Residency Learning Curves Using Natural Language Processing: Retrospective Cohort Study

Quantifying Emergency Medicine Residency Learning Curves Using Natural Language Processing: Retrospective Cohort Study

Authors of this article:

Carl Preiksaitis1 Author Orcid Image ;   Joshua Hughes1 Author Orcid Image ;   Rana Kabeer1 Author Orcid Image ;   William Dixon1 Author Orcid Image ;   Christian Rose1 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Shaw J, Mand S, Williams S, Cico S, Bailes C, Alvarez A, Promes S, Wagner J, Schnapp B. Practical Applications of Generative Artificial Intelligence for Clinical Teaching in the Emergency Department. AEM Education and Training 2026;10(2) View
  2. Hill J, Thompson K, Brower C, Boyer L. Educational Impact of Physicians‐in‐Triage: Characteristics of Patients Seen and Dispositioned. AEM Education and Training 2026;10(2) View