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

Corrigenda and Addenda (2)

Published on in Vol 10 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/57594, first published .
Correction: How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge Assessment

Correction: How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge Assessment

Correction: How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge Assessment

Authors of this article:

Aidan Gilson1, 2 Author Orcid Image ;   Conrad W Safranek1 Author Orcid Image ;   Thomas Huang2 Author Orcid Image ;   Vimig Socrates1, 3 Author Orcid Image ;   Ling Chi1 Author Orcid Image ;   Richard Andrew Taylor1, 2 Author Orcid Image ;   David Chartash1, 4 Author Orcid Image
Article Authors Cited by (1) Tweetations (2) Metrics

    Corrigenda and Addenda

    1Section for Biomedical Informatics and Data Science, Yale University School of Medicine, New Haven, CT, United States

    2Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT, United States

    3Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States

    4School of Medicine, University College Dublin, National University of Ireland, Dublin, Dublin, Ireland

    *these authors contributed equally

    Corresponding Author:

    David Chartash, PhD

    Section for Biomedical Informatics and Data Science

    Yale University School of Medicine

    300 George Street

    Suite 501

    New Haven, CT, 06511

    United States

    Phone: 1 203 737 5379

    Email: david.chartash@yale.edu



    In “How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge” (MIR Med Educ 2023;9:e45312) three additions were made to enhance discoverability.

    The title originally appeared as:

    How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment

    And has been changed to:

    How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge

    In the “Objective” section of the Abstract, the following sentence:

    This study aimed to evaluate the performance of ChatGPT on questions within the scope of the United States Medical Licensing Examination Step 1 and Step 2 exams, as well as to analyze responses for user interpretability.

    Has been changed to read as:

    This study aimed to evaluate the performance of ChatGPT on questions within the scope of the United States Medical Licensing Examination (USMLE) Step 1 and Step 2 exams, as well as to analyze responses for user interpretability.

    Finally, the abbreviation “USMLE” has been added to the Keywords section.

    The correction will appear in the online version of the paper on the JMIR Publications website on February 27, 2024 together with the publication of this correction notice. Because this was made after submission to PubMed, PubMed Central, and other full-text repositories, the corrected article has also been resubmitted to those repositories.

    This is a non–peer-reviewed article. submitted 20.02.24; accepted 20.02.24; published 27.02.24.

    Copyright

    ©Aidan Gilson, Conrad W Safranek, Thomas Huang, Vimig Socrates, Ling Chi, Richard Andrew Taylor, David Chartash. Originally published in JMIR Medical Education (https://mededu.jmir.org), 27.02.2024.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Education, is properly cited. The complete bibliographic information, a link to the original publication on https://mededu.jmir.org/, as well as this copyright and license information must be included.