When researchers at four medical centers in Poland introduced AI to help endoscopists detect precancerous polyps, the tool worked, and detection rates improved. But the same endoscopists, when tested without the AI, performed measurably worse than they had before the technology arrived, a finding now shaping how Benjamin Schwartz, MD, president of academic delivery at Banner Health, thinks about AI deskilling academic medicine Banner is confronting as adoption accelerates.
What the Underlying Study Behind This AI Deskilling Academic Medicine Banner Concern Found
That finding, from a study published in Lancet Gastroenterology & Hepatology, is the kind of data Schwartz is watching closely as health systems accelerate AI adoption across clinical workflows. “Before AI was introduced, the same endoscopists were detecting adenomas at a rate of roughly 28%,” Schwartz said in the podcast interview. “And after AI was introduced, which was intended to help them detect suspicious lesions, when they did it without AI, their detection rate dropped to 22%. So what’s implied by that is that there’s a risk that, relying on the machine, you lose the practice of the skills that you’ve had and that you might lose them.”
Why the Detection Rate Drop Reveals a Hidden Cost
The study involved roughly 1,400 procedures. The AI was working as designed: total detection rates held because the tool was present. What changed was something harder to measure, the clinician’s independent ability when the tool was not, illustrating how a technology’s apparent success can mask a quieter erosion of underlying skill.
How This AI Deskilling Academic Medicine Banner Concern Shaped an Internal Debate
The pattern is playing out in Banner’s own AI decisions, including a debate the system had over ambient listening technology, tools that capture caregiver-patient interactions and automatically populate the clinical record. The productivity benefits of those tools are clear, but the training implications are more complicated.
The Specific Fear Banner’s Leadership Debated
“We debated quite a bit whether or not the trainee should have that for fear that they would never learn the skill of writing those notes and without that skill, they would become completely dependent on the machine,” Schwartz said.
Why Banner Ultimately Allowed Trainees to Use the Technology
But the deskilling fear shouldn’t stop progress. Technology continues to evolve and the skills behind it change as well, and health systems are balancing forward progress with ensuring their teams have foundational skills. “The outcome of that discussion was that they should have the technology,” Schwartz said.
The Scrutiny Framework Banner Now Applies to Every AI Intervention
“But we go through every AI intervention through that scrutiny to make sure that we’re not creating a next generation without any of the basic skills that would be needed if the machine weren’t there,” he said. This framework positions Banner’s approach as neither blanket AI restriction nor unchecked adoption, but a deliberate, case-by-case evaluation of what skill Banner risks losing against what capability the technology provides.
What This AI Deskilling Academic Medicine Banner Approach Means Going Forward
Given the concrete, measurable evidence from the Polish endoscopy study, academic medical centers introducing similar AI diagnostic or documentation tools may want to build in periodic “AI-off” skill assessments for trainees, mirroring the kind of testing that revealed the endoscopists’ declining independent performance in the first place. Banner’s decision to still allow ambient listening technology for trainees, despite acknowledging the deskilling risk, suggests health systems don’t need to choose between AI adoption and skill preservation, but may need structured evaluation processes to pursue both simultaneously.
What to Watch Going Forward
As more academic medical centers confront similar tradeoffs between AI-driven efficiency and trainee skill development, industry observers will likely watch whether additional deskilling studies emerge across other clinical domains beyond endoscopy, and whether health systems adopt formal evaluation frameworks similar to Banner’s before introducing new AI tools to trainees. Given Schwartz’s explicit framing of this as an ongoing scrutiny process rather than a one-time decision, this AI deskilling academic medicine Banner approach may offer other academic health systems a practical model for navigating AI adoption without inadvertently producing a generation of clinicians who lack the foundational skills needed when the technology isn’t available.
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