Are you curious how Artificial Intelligence (AI) is transforming medical education, especially its impact on faculty teaching and student learning? Join the upcoming IAMSE Fall webinar series entitled “Brains, Bots, and Beyond: Exploring AI’s Impact on Medical Education” to learn about the intersection of AI and medical education. Over five sessions, we will cover topics ranging from the basics of AI to its use in teaching and learning essential biomedical science content.
The series begins on September 7 with a presentation by Homayoun Valafar to define AI and machine learning. The series will continue on September 14 with a discussion by Erkin Otles and Cornelius James on strategies to prepare our trainees to appropriately utilize AI in their future healthcare jobs. On September 21st, Michael Paul Cary and Sophia Bessias will present on critical ethical issues, including the potential for unintended bias and disparities arising from AI. Finally, Dina Kurzweil and Bill Hersh will wrap up the series on September 28th and October 5th, respectively, with practical tips for educators and learners alike to utilize AI to maximize teaching and learning. Don’t miss this exciting opportunity to join the conversation on the future of AI in medical education.
Transforming Healthcare Together: Empowering Health Professionals to Address Bias in the Rapidly Evolving AI-Driven Landscape
Presenters: Sophia Bessias, MPH, MSA and Michael Paul Cary, Jr., PhD, RN, FAAN
Session Date & Time: September 21, 2023 at 12pm Eastern
Session Description: As the interest in utilizing AI/machine learning in healthcare continues to grow, healthcare systems are adopting algorithms to enhance patient care, alleviate clinician burnout, and improve operational efficiency. However, while these applications may appear promising, they also carry certain risks, including the potential to automate and reinforce existing health disparities.
During this seminar, we will introduce the ABCDS Oversight framework developed at Duke Health. This comprehensive framework focuses on the governance, evaluation, and monitoring of clinical algorithms, providing participants with practical guidance to ensure the responsible implementation of AI/ML. Specifically, we will highlight how high-level principles can be translated into actionable steps for developers, allowing them to maximize patient benefit while minimizing potential risks.
Read the full session description here.