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

Artificial Intelligence (AI) in Medical Education (83) Generative Language Models Including ChatGPT (325) e-Learning and Digital Medical Education (705) Natural Language Processing (706) Testing and Assessment in Medical Education (55) Design of Educational Technology (39) Chatbots and Conversational Agents (543) Theme Issue: ChatGPT and Generative Language Models in Medical Education (86)

Published on in Vol 10 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/56342, first published .
Assessing the Ability of a Large Language Model to Score Free-Text Medical Student Clinical Notes: Quantitative Study

Assessing the Ability of a Large Language Model to Score Free-Text Medical Student Clinical Notes: Quantitative Study

Assessing the Ability of a Large Language Model to Score Free-Text Medical Student Clinical Notes: Quantitative Study

Authors of this article:

Harry B Burke1 Author Orcid Image ;   Albert Hoang1 Author Orcid Image ;   Joseph O Lopreiato1 Author Orcid Image ;   Heidi King2 Author Orcid Image ;   Paul Hemmer1 Author Orcid Image ;   Michael Montgomery1 Author Orcid Image ;   Viktoria Gagarin1 Author Orcid Image
Article Authors Cited by (1) Tweetations (1) Metrics

Harry B Burke   1 , MD, PhD ;   Albert Hoang   1 , PhD, DSc ;   Joseph O Lopreiato   1 , MD ;   Heidi King   2 , MS ;   Paul Hemmer   1 , MD ;   Michael Montgomery   1 , BS ;   Viktoria Gagarin   1 , MD

1 Uniformed Services University of the Health Sciences, Bethesda, MD, United States

2 Defense Health Agency, Falls Church, VA, United States

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