Artificial Intelligence ( AI) ‐enhanced journal club: integrating podcasts, AI , and social media in medical education
Objective To develop and evaluate an artificial intelligence (AI)‐assisted, multimedia journal club model that improves accessibility by transforming scientific articles into on‐demand educational formats distributed through digital platforms. Subjects and Methods This prospective single‐centre educational evaluation was conducted at King's College Hospital (London). Six AI‐enhanced journal club sessions were implemented between June and September 2025. Peer‐reviewed urology articles were processed using AI‐assisted tools to generate podcasts, slide presentations, short‐form video summaries, question‐and‐answer materials, and visual infographics. Content was distributed through clinician‐facing platforms including YouTube, Spotify, and social media. Two surveys evaluated educational impact: a format‐specific survey completed by 48 participants and a second survey assessing overall perceptions of the AI‐enhanced journal club completed by 19 participants. Results The AI‐generated podcast was the most preferred format (62.5%), followed by video summaries (16.7%) and slide presentations (8.3%). Participants reported greater engagement compared with traditional journal clubs, with 54.2% selecting the response option ‘much more engaged’. Format‐specific ratings were consistently high, particularly for podcasts (mean [standard deviation, SD] score for engagement 4.6 [0.6] and for clarity 4.8 [0.5]). In the second survey, overall ratings of the AI‐enhanced journal club were high across all domains, including engagement (mean [SD] score 4.8 [0.4]), clarity (mean [SD] score 4.8 [0.5]), time efficiency (mean [SD] score 4.7 [0.6]), and overall preference (mean [SD] score 4.9 [0.3]). Conclusion An AI‐assisted, multimodal journal club integrating podcasts, slides, and digital dissemination was associated with improved participant‐reported engagement and accessibility. This scalable model supports asynchronous learning and may complement traditional journal club formats in postgraduate medical education.
Authors
- Ömer Onur Çakır (ORCID: https://orcid.org/0000-0001-7499-7227)
- Francesca Kum (ORCID: https://orcid.org/0000-0002-4978-2547)
- Fabio Castiglione (ORCID: https://orcid.org/0000-0001-8046-1963)
- Maria Satchi (ORCID: https://orcid.org/0000-0002-2598-0899)
- Edward James Bass (ORCID: https://orcid.org/0000-0002-4923-572X)
- Tet Yap (ORCID: https://orcid.org/0000-0003-4148-6548)
- Shyaw Mahmood Ahmed (ORCID: https://orcid.org/0009-0008-5634-4829)
- Nicholas Tobias Johannes Raison (ORCID: https://orcid.org/0000-0003-0496-4985)
- Kalyan Gudaru (ORCID: https://orcid.org/0000-0003-2335-7419)
- Muhammed Arif İbiş (ORCID: https://orcid.org/0000-0001-8581-2101)
- Sanjith Gnanappiragasam
- Molly M Nichols (ORCID: https://orcid.org/0000-0002-7558-2901)
- Kawa Omar
- Azhar Khan (ORCID: https://orcid.org/0009-0005-5912-7182)
- Mohamed Abouelenein
Institutions
- St Thomas' Hospital (GB)
- Ankara University (TR)
- King's College London (GB)
- Fetal Medicine Foundation (GB)
- King's College Hospital (GB)
Publication Details
- Journal
- British Journal of Urology
- Published
- 2026-10-06
- DOI
- https://doi.org/10.1111/bju.70479
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00