- Education Partnership To Improve Artificial Intelligence Practices
- How Healthcare Can Prep for Artificial Intelligence, Machine Learning
Specifically, the authors proposed that medical educators should treat AI as a critical component of medical practice to be introduced early and integrated with the other core components of medical school curricula. By equipping graduating medical students with this knowledge, educators will help ensure that students possess the skills to solve challenges at the intersection of AI and medicine, they stated.
But, according to the authors, current proposals for AI education integration are superficial, framing the training as an “added layer” to existing curricula rather than a core concept of medicine. They rejected this framework, proposing instead that medical schools view AI as a fundamental component of medical practice that should be deeply integrated into UME.
They further noted that the transition to integrate AI “will require curricular modifications, reprioritizing resources, and analysis of the relationship of AI to existing curricular components of UME.”
In their commentary, the researchers outlined the transition and how AI can be presented as a critical skill for medical students.
First, they presented the ‘analytics hierarchy,’ an approach to stratifying AI methods in terms of complexity and relevance. The hierarchy has three tiers, each related to a function of AI: descriptive analytics, predictive analytics, and prescriptive analytics.
The tiers can be conceptualized as a pyramid with three sections: descriptive analytics at the bottom, predictive in the middle, and prescriptive at the top. This visualization helps describe how understanding the concepts in the lower layers provides the foundational knowledge necessary for understanding the concepts in the upper layers.
By integrating AI training using this hierarchy, educators can ensure that relevant curricula are devoted to essential AI concepts that can be built upon longitudinally throughout UME, the researchers explained.
They also noted that this approach does not require radically changing the existing UME curriculum. But it requires careful consideration of how UME traditionally prioritizes memorization over the retrieval, integration, and critical appraisal of information.
Medical educators will also need to form new partnerships outside of traditional medical education to bolster AI curricula. By collaborating with experts, such as data scientists, engineers, and lawyers, and adapting existing resources from fields that traditionally teach AI concepts, such as computer science, statistics, and industrial engineering, medical educators may have more success building AI content tailored to UME, the authors noted.
Providing an early foundation in AI for all medical students will also help position those particularly interested in medical AI to pursue advanced training, enabling the development of a group of clinicians with deep expertise in AI.
“We believe the time to integrate AI education into UME is now so that physicians have an opportunity to shape an emerging technology. We must do so by treating AI as a fundamental toolset of medical practice, understanding that its core concepts will touch all parts of medicine… This approach will prepare medical trainees to thrive in their future practice environments and will enable the medical profession to guide the development and incorporation of AI to the benefit of patients and populations,” the authors concluded.
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