<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Yao MS</submitter><funding>NIMHD NIH HHS</funding><funding>NIGMS NIH HHS</funding><pagination>e63602</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12017604</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>11</volume><pubmed_abstract>&lt;h4>Background&lt;/h4>As artificial intelligence and machine learning become increasingly influential in clinical practice, it is critical for future physicians to understand how such novel technologies will impact the delivery of patient care.&lt;h4>Objective&lt;/h4>We describe 2 trainee-led, multi-institutional datathons as an effective means of teaching key data science and machine learning skills to medical trainees. We offer key insights on the practical implementation of such datathons and analyze experiences gained and lessons learned for future datathon initiatives.&lt;h4>Methods&lt;/h4>We detail 2 recent datathons organized by MDplus, a national trainee-led nonprofit organization. To assess the efficacy of the datathon as an educational experience, an opt-in postdatathon survey was sent to all r</pubmed_abstract><journal>JMIR medical education</journal><pubmed_title>Leveraging Datathons to Teach AI in Undergraduate Medical Education: Case Study.</pubmed_title><pmcid>PMC12017604</pmcid><funding_grant_id>F30 MD020264</funding_grant_id><funding_grant_id>T32 GM146636</funding_grant_id><pubmed_authors>Liou L</pubmed_authors><pubmed_authors>Yao MS</pubmed_authors><pubmed_authors>Sun C</pubmed_authors><pubmed_authors>Huang L</pubmed_authors><pubmed_authors>Leventhal E</pubmed_authors><pubmed_authors>Stephen SJ</pubmed_authors></additional><is_claimable>false</is_claimable><name>Leveraging Datathons to Teach AI in Undergraduate Medical Education: Case Study.</name><description>&lt;h4>Background&lt;/h4>As artificial intelligence and machine learning become increasingly influential in clinical practice, it is critical for future physicians to understand how such novel technologies will impact the delivery of patient care.&lt;h4>Objective&lt;/h4>We describe 2 trainee-led, multi-institutional datathons as an effective means of teaching key data science and machine learning skills to medical trainees. We offer key insights on the practical implementation of such datathons and analyze experiences gained and lessons learned for future datathon initiatives.&lt;h4>Methods&lt;/h4>We detail 2 recent datathons organized by MDplus, a national trainee-led nonprofit organization. To assess the efficacy of the datathon as an educational experience, an opt-in postdatathon survey was sent to all r</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025 Apr</publication><modification>2026-03-11T03:55:47.413Z</modification><creation>2025-07-02T03:04:52.778Z</creation></dates><accession>S-EPMC12017604</accession><cross_references><pubmed>40239213</pubmed><doi>10.2196/63602</doi></cross_references></HashMap>