Anki Tagger: A Generative AI Tool for Aligning Third-Party Resources to Preclinical Curriculum

Tricia Pendergrast; Zachary Chalmers

doi:10.2196/48780

Anki Tagger: A Generative AI Tool for Aligning Third-Party Resources to Preclinical Curriculum

JMIR Med Educ. 2023 Sep 20:9:e48780. doi: 10.2196/48780.

Authors

Tricia Pendergrast^#¹, Zachary Chalmers^#²

Affiliations

¹ Department of Anesthesiology, University of Michigan Medicine, Ann Arbor, MI, United States.
² Northwestern University Feinberg School of Medicine, Chicago, IL, United States.

^# Contributed equally.

PMID: 37728965
PMCID: PMC10551781
DOI: 10.2196/48780

Abstract

Using large language models, we developed a method to efficiently query existing flashcard libraries and select those most relevant to an individual's medical school curricula.

Keywords: AI; Anki; ChatGPT; artificial intelligence; flashcards; large language models; undergraduate medical education.

©Tricia Pendergrast, Zachary Chalmers. Originally published in JMIR Medical Education (https://mededu.jmir.org), 20.09.2023.