'Slowing down when you should': optimising the translation of artificial intelligence into medical school curricula

• Main Authors: Kitto, Simon, Ng, Yih Yng, Cleland, Jennifer
• Other Authors: Lee Kong Chian School of Medicine (LKCMedicine)
• Format: Article
• Language: English
• Published: 2024
• Subjects: Medicine, Health and Life Sciences; Artificial intelligence; Medical school curricula
• Online Access: https://hdl.handle.net/10356/174756
• Institution: Nanyang Technological University

This is an exciting and anxious time for medicine and medical education as innovations and applications of artificial intelligence (AI) in both domains proliferate at a rapid, dizzying pace. In this article, we call for a considered approach to the design and implementation of AI usage by students in undergraduate medical education (UGME). To do so, we adopt the metaphor of ‘slowing down when you should’ from the intraoperative surgical decision making literature,[1] where surgeons mobilise cognitive resources during moments in surgery to avoid errors and choose the best course of action. Like surgery, this would mean that ‘moments’ should be taken during the implementation of AI into medical school curricula to assess and plan subsequent steps. We briefly explain in this short article why doing so is useful. Second, and following our first point, we propose adopting frameworks from Implementation Science (IS) to guide the successful incorporation of AI teaching into medical education, as this enables consideration of best evidence medical education practices while being mindful of patient safety concerns.