University Medical Schools Train Students to Better Understand AI

As artificial intelligence shows staying power in higher education, medical schools are prepping the next generation.

Nathan Eddy works as an independent filmmaker and journalist based in Berlin, specializing in architecture, business technology and IT. He is a graduate of Northwestern University’s Medill School of Journalism.

As the use of artificial intelligence and machine learning continue to expand in higher education, the next generation of clinicians will need to be more knowledgeable about such technologies. This raises challenges and opportunities for medical schools.

Instructors are integrating AI and ML into curricula to train medical students to practice medicine in the real world, says Dr. Bernard Chang, dean for medical education at Harvard Medical School, which is creating an AI in medicine doctorate track.

“We know AI is going to transform how healthcare is delivered. It already is doing that,” he says. “I don’t believe that AI will replace human physicians, but those who use AI will be so much more capable than those who don't.”

The focus is on teaching AI to medical students from the ground up, he adds.

“They don’t have a way of practicing yet. They’re learning how to be physicians of the future,” Chang says. “What we want to do is get them started off on the right foot.”

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Understanding How AI Can Impact Medical Schools

The ability to train incoming university medical students and, eventually, healthcare workers to embrace and interrogate this new technology will better determine its influence and how it can be used in a positive way to promote health outcomes, says Dr. Aditee Narayan, associate dean for curricular affairs at Duke University School of Medicine.

“It is important to tie education on AI to the reasons that folks went into medicine in the first place,” she says.

For medical schools to be most effective in teaching future physicians about AI, they must support the clinical and research missions of the institution in the AI space, she adds.

“What will work best is to see the ways that our institution is going to prioritize the use of AI in clinical operations and patient care, and then to align our curricular additions with those new initiatives,” Narayan says.

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Demystifying the AI Models That Affect Higher Education

At the University of Texas at San Antonio, students can choose to take a break from the medical program to spend a year in an immersive experience in the AI field. They take foundational courses in AI and elective courses based on their specialty focus.

Physicians need a good understanding of the AI models being used in different clinical scenarios, says Dr. Dhireesha Kudithipudi, founding director of the university’s MATRIX AI Consortium and leader of the team that developed the AI courses for the unique doctor of medicine/Master of Science dual-degree program launched by the university last year.

“Whether it is in anomaly detection, image processing or decision-making, students need an understanding of where the models are coming from and what types of AI models are being used in these scenarios,” Kudithipudi says.

From her perspective, students must also understand how AI models are used to develop certain solutions.

“Quite often, these AI models are not designed in an inclusive way, or they have some hidden biases in the models,” she says. “In those contexts, human intervention becomes important to mitigate the blind spots that AI has and avoid these biases being translated into actual impact.”

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