A group of educators and researchers from University of Michigan’s Medical School called for integrating AI and machine learning into the medical school curriculum in a recent Cell Reports Medicine article. They claimed that AI and its usage is relevant to nearly all areas of clinical practice and that current medical school graduates are left “under prepared” to interface effectively with these technologies.
Two of the article’s authors, Erkin Ötleş, a machine learning researcher and current medical and Ph.D. Student at the University of Michigan, and Jim Woolliscroft, a former Michigan Medical School dean, followed up on these ideas in an interview in Stat Magazine.
Ötleş expressed concern about medical providers who use AI systems without the understanding needed to ask questions and validate the output, as this can lead to unchecked system errors or biases that cause harm. “We’re going to be at a point where we’re not going to be able to catch up and be able to call out the technology defects or flaws,” Ötleş said. “Without being armed with that set of foundational knowledge into how these things work, we’re going to be at a disadvantage.”
Ötleş and Woolliscroft also described the current training in AI and machine learning in medical school as predominantly student-driven. Interested students are mostly left to independently seek out additional degrees, courses, or electives as there is currently no systemic approach to teach medical students about AI in an integrated and meaningful way. “Medical students don’t know about this stuff, and they need to see it as basic as pharmacology and physiology. Already, machine learning algorithms, and more generally AI, are essentially ubiquitous,” Woolliscroft said.
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Two of the article’s authors, Erkin Ötleş, a machine learning researcher and current medical and Ph.D. Student at the University of Michigan, and Jim Woolliscroft, a former Michigan Medical School dean, followed up on these ideas in an interview in Stat Magazine.
Ötleş expressed concern about medical providers who use AI systems without the understanding needed to ask questions and validate the output, as this can lead to unchecked system errors or biases that cause harm. “We’re going to be at a point where we’re not going to be able to catch up and be able to call out the technology defects or flaws,” Ötleş said. “Without being armed with that set of foundational knowledge into how these things work, we’re going to be at a disadvantage.”
Ötleş and Woolliscroft also described the current training in AI and machine learning in medical school as predominantly student-driven. Interested students are mostly left to independently seek out additional degrees, courses, or electives as there is currently no systemic approach to teach medical students about AI in an integrated and meaningful way. “Medical students don’t know about this stuff, and they need to see it as basic as pharmacology and physiology. Already, machine learning algorithms, and more generally AI, are essentially ubiquitous,” Woolliscroft said.
Click here to access the rest of this blog.
