Stephanie A. Lareau, Justin G. Gardner, Carver M. Haines
Introduction Podcasts are a popular medical education tool, especially for the review of journal articles, but production requires significant resources. The objectives of this study were to determine the feasibility of a trained artificial intelligence (AI) model generating well-received educational podcasts based on journal articles and to assess listener comprehension with knowledge-based questions. Methods Google Gemini 2.0 Flash AI was trained on 12 Wilderness and Environmental Medicine journal articles to generate 3 distinct educational podcasts (4 articles per podcast). These podcasts featured 2 AI-generated host voices in a journal club format. Participants from the Wilderness Medical Society Student/Resident Committee, the Virginia Tech Carilion Wilderness Medicine Fellowship Program, and a convenience sample of students and residents were randomly assigned to listen to 1 podcast, blinded to its AI origin. They then completed a 5-point Likert perception survey and a knowledge assessment with continuing medical education-style multiple-choice questions based on the discussed articles. Results Thirty-one participants completed the study. Participant perception was highly positive: 87.5% agreed or strongly agreed that the content was accurate and relevant (mean Likert score 4.37), and 81.25% agreed or strongly agreed that the podcasts aided journal article review and learning (mean Likert score 4.14). The mean knowledge assessment score was 88.7% correct (SD 5.0%). Conclusions Despite limitations, including a small sample size and lack of a control group, AI-generated podcasts were positively received and effectively conveyed educational content, as evidenced by high knowledge assessment scores.