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

Artificial Intelligence (AI) in Medical Education (186) New Methods and Approaches in Medical Education (378) Graduate and Postgraduate Education for Health Professionals (135) Testing and Assessment in Medical Education (94) Specialist Training for Doctors (63)

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 Tweetations Metrics

Carl Preiksaitis   1 , MD, MEd ;   Joshua Hughes   1 , MD ;   Rana Kabeer   1 , MD, MPH ;   William Dixon   1 , MD, MSED ;   Christian Rose   1 , MD

1 Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, United States

Corresponding Author:

  • Carl Preiksaitis, MD, MEd
  • Department of Emergency Medicine, Stanford University School of Medicine
  • 900 Welch Road, Suite 350
  • Palo Alto, CA 94304
  • United States
  • Phone: 1 650-723-6576
  • Fax: 1 650-723-0121
  • Email: cpreiksaitis@stanford.edu