Abstract: FR-PO007
A Multicenter Survey on Artificial Intelligence in Nephrology Education: Insights from Mayo Clinic Fellows
Session Information
- Classroom to Bedside: Transforming Medical Education
October 25, 2024 | Location: Exhibit Hall, Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Educational Research
- 1000 Educational Research
Authors
- Sheikh, M. Salman, Mayo Clinic Minnesota, Rochester, Minnesota, United States
- Thongprayoon, Charat, Mayo Clinic Minnesota, Rochester, Minnesota, United States
- Craici, Iasmina, Mayo Clinic Minnesota, Rochester, Minnesota, United States
- Qureshi, Fawad, Mayo Clinic Minnesota, Rochester, Minnesota, United States
- Mao, Michael A., Mayo Clinic in Florida, Jacksonville, Florida, United States
- Hommos, Musab S., Mayo Clinic Arizona, Scottsdale, Arizona, United States
- Prendergast, Mary B., Mayo Clinic in Florida, Jacksonville, Florida, United States
- Sukumaran Nair, Sumi, Mayo Clinic Arizona, Scottsdale, Arizona, United States
- Kashani, Kianoush, Mayo Clinic Minnesota, Rochester, Minnesota, United States
- Cheungpasitporn, Wisit, Mayo Clinic Minnesota, Rochester, Minnesota, United States
Background
Artificial Intelligence (AI) has the potential to support clinical practice, medical education, and research. This study aimed to describe current perceptions and utilization of AI among nephrology fellows in three programs, and perform needs assessment for an educational intervention focused on AI learning.
Methods
An online survey was sent to 23 learners including fellows in general nephrology, kidney transplant, and onco-nephrology at the Mayo Clinic's three sites: Minnesota, Arizona, and Florida. The survey evaluated the current state of AI perceptions, utilization, and integration in practice. It also identified attitudes toward AI training, and perceived barriers to AI adoption in nephrology fellowship education.
Results
Of the 23 trainees, 21 completed the survey (response rate of 91%). The survey revealed that 76% of respondents have never or rarely used AI in their clinical or research activities, and none had formal AI education. However, respondents expressed a strong acknowledgment of AI's significance within nephrology, with 76% of respondents rating AI's current relevance as moderately to highly relevant to the field and 76% of fellows expressed moderate to high interest in receiving targeted AI. The majority of trainees identified interactive workshops as the preferred method of delivering AI training. The majority of fellows identified limited knowledge as the primary barrier to AI adoption. Optimism about AI's potential in nephrology was pronounced, especially for predictive modeling (86%) and diagnostic imaging (81%). However, confidence in AI for clinical decision-making remained cautious, with 33% neutral and 48% uncertain.
Conclusion
The findings underscore a significant interest among nephrology fellows in AI education, along with a critical need for formal education and training. The enthusiasm for AI's potential contrasts with a cautious perspective towards its current use in clinical decision-making, highlighting the necessity for educational initiatives that bridge the knowledge gap and foster confidence in AI technologies in Nephrology fellowship.