| Autor: |
Andrew Dennis Smith, Hari Trivedi, Houman Sotoudeh, Jordan D Perchik, Marcelo Straus Takahashi, Paul H Yi, Srini Tridandapani, Steven A Rothenberg, Vishwa Parekh |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Journal of Global Radiology, Vol 10, Iss 1 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2372-8418 |
| DOI: |
10.7191/jgr.783 |
| Popis: |
Introduction: Applications of artificial intelligence (AI) in radiology continue to increase every year, however most radiology residencies lack a dedicated AI education curriculum. Fundamental AI education resources are even more sparse for trainees in low- to middle-income countries and under-resourced healthcare systems. The AI Literacy Course assesses the effectiveness and scalability of a free, remote AI education curriculum to increase understanding of fundamental AI terms, methods, and applications in radiology among radiology trainees in the United States and internationally. Method: A week-long AI in radiology literacy course for radiology trainees was held October 3-7, 2022. Ten 30-minute lectures utilizing a remote learning format covered basic AI terms and methods, clinical applications of AI in radiology by three different subspecialties, and special topics lectures. A proctored, hands-on clinical AI session allowed participants to directly use an FDA-cleared, AI-assisted viewer and reporting system for advanced cancer. Pre- and post-course electronic surveys were distributed to assess participants’ knowledge of AI terminology and applications, as well as their interest in AI education. Results: A total of 25 residency programs throughout the US participated in the course with participants attending from 10 countries. An average of 150 participants viewed the course per day. Nearly all participants reported insufficient exposure to AI in their radiology training (95.8%). Participant knowledge of fundamental AI terms and methods increased after completion of the course, with an average pre-course evaluation of 8.3/15 and a post-course evaluation of 10.0/15 (p=0.01). Conclusion: The scalability of the AI Literacy Course demonstrates a viable model to bring accessible fundamental AI education to radiology trainees in the United States and internationally. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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