Leveraging Datathons to Teach AI in Undergraduate Medical Education: Case Study
The datathon proved to be an effective and cost-effective means of providing medical trainees the opportunity to collaborate on data-driven projects in health care. Participants agreed that datathons improved their ability to generate clinically meaningful insights from data. Our results suggest that datathons can serve as valuable and effective educational experiences for medical trainees to become better skilled in leveraging data science and artificial intelligence for patient care.
Authors
- Emily Leventhal (ORCID: https://orcid.org/0000-0002-1602-634X)
- Steve J. Stephen (ORCID: https://orcid.org/0009-0009-7135-4846)
- Lathan Liou (ORCID: https://orcid.org/0000-0002-8066-5947)
- Clara Sun
- Michael S. Yao (ORCID: https://orcid.org/0000-0002-7008-6028)
- Lawrence Huang (ORCID: https://orcid.org/0000-0003-1675-9962)
Institutions
- Brown University (US)
- Rusk Rehabilitation (US)
- University of Rochester (US)
- Case Western Reserve University (US)
- University of Pennsylvania (US)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- JMIR Medical Education
- Published
- 2025-02-26
- DOI
- https://doi.org/10.2196/63602
- Citations
- 1
- Primary Topic
- Biomedical and Engineering Education
- Type
- article
- Field-Weighted Citation Impact
- 0.67