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

Journals

  1. Jamieson A, Holcomb M, Dalton T, Campbell K, Vedovato S, Shakur A, Kang S, Hein D, Lawson J, Danuser G, Scott D. Rubrics to Prompts: Assessing Medical Student Post-Encounter Notes with AI. NEJM AI 2024;1(12) View