AI Is Reshaping How Doctors Train And What They Become
核心洞察
AI is forcing medical schools to rapidly reconsider physician training, with educators describing this as a "key inflection point for medical education," according to NYU's Marc Triola.
New AI tools enable personalized assessment of trainees' communication and clinical reasoning skills, areas that have long been difficult to evaluate directly.
Concerns about "never-skilling" and "deskilling" arise as trainees offload cognitive tasks to AI, though some experts argue not all deskilling is harmful and may free capacity for essential skills.
For generations, physicians have trained through a cognitive apprenticeship model—practicing under supervision, receiving feedback, and learning how experienced clinicians solve complex problems. But as artificial intelligence increasingly performs the cognitive tasks through which clinicians have traditionally learned to think, the medical community is being forced to quickly reconsider how it trains physicians, even as many of the technology's educational effects remain unknown.
Marc Triola, NYU Grossman School of Medicine (搜索)'s senior associate dean for education, put it plainly: "We are at a key inflection point for medical education."
How AI Could Transform Medical Training
Training large groups of medical students, residents, and fellows has traditionally required educators to teach to the average learner and assess what is easiest to measure: knowledge recall. AI creates opportunities to evaluate trainees' capabilities and personalize their training in ways that were previously impossible.
At Mount Sinai, educators are using AI scribe transcripts to analyze trainees' communication skills and provide targeted feedback. A team at the University of Pennsylvania is evaluating clinical reasoning by analyzing conversations among internal medicine residents and their colleagues. As Verity Shaye, assistant dean for education at NYU, explained, medical educators have long struggled to assess communication and reasoning skills directly.
Procedural training may also change dramatically. Traditionally, competency has been assessed by the number of cases a trainee completes, rather than how well they perform them. Researchers at Stanford are using sensor technologies to quantify surgical technique and provide data-driven coaching. At Kaiser Permanente East Bay, otolaryngologist Alexander Rivero is using AI to collect and aggregate ENT residents' post-operative debriefs and other learning artifacts into longitudinal learning portfolios.
AI can also help connect classroom and clinical learning. Educators at NYU use AI to deliver "educational nudges," such as automatically sending trainees key articles related to the patients they see on the wards. Meanwhile, AI simulations can broaden trainees' exposure to diverse populations and conditions they may not encounter in their own settings.
More broadly, by freeing trainees from rote memorization and "scut" work that has long occupied their time and headspace, AI could allow them to spend more time with patients, refine their reasoning skills, and engage in more cognitively demanding tasks. Yet many of these same tasks are how trainees have traditionally developed expertise.
The Risks of Training With AI
Educational researchers have long argued that expertise develops through effortful learning and deliberate practice. Trainees who offload key tasks to AI—such as writing clinical notes, summarizing medical records, synthesizing medical literature, interviewing patients, and formulating differential diagnoses—may fail to develop key capabilities ("never-skilling"), lose previously developed skills ("deskilling"), and develop incorrect practices ("mis-skilling").
Research on cognitive offloading suggests these concerns are real. When people habitually rely on GPS, spatial memory diminishes. When they stop writing notes by hand, they retain less information. Outsourcing cognitive work can change how people learn, what they remember, and how they think critically.
Yet concerns about technology eroding physicians' skills are hardly new. Earlier generations of doctors opposed learning from medical textbooks, arguing that conjuring knowledge from memory forced deeper reflection. Fletcher Bell, internal medicine chief resident at UCSF, noted that residents who spend up to 80 hours in the hospital each week have ample learning opportunities.
At the same time, not all deskilling is bad. Some may be necessary to make room for learning essential skills. Cornelius James, a primary care physician and medical education researcher at the University of Michigan, explained: "We risk losing the trust of learners if we force them to learn things they no longer need to learn."
Still, AI may represent a different kind of challenge as it performs many of the cognitive tasks through which clinicians have traditionally learned to think, such as critically appraising research studies or developing treatment plans. The challenge is determining which activities can be offloaded to AI and which remain essential.
What We Still Don't Know
Despite the intensity of the debate, the evidence base remains thin. At Washington University, John Davis, an internal medicine resident, describes a small but vocal group of fervent users who claim it's borderline malpractice not to use AI, alongside another group that feels these tools can't be trusted at all. Most of his colleagues fall somewhere in between.
Davis also describes widespread "social posturing," whereby some colleagues signal that they arrived at their answers or completed their work independently. And despite countless discussions about deskilling or never-skilling, the evidence largely rests on a single, limited study of Polish gastroenterologists using adenoma detection software—hardly enough to draw broad conclusions about AI and medical training.
Most of all, the medical community is unsure what future it is preparing trainees for. The long-held belief that doctors plus AI are better than either alone no longer appears universally true.
Responding Before All the Answers Are In
Medical education has been slow to respond, in part because of this uncertainty, and in part because many educators feel unqualified to teach AI. Laurah Turner, an anthropologist and associate dean at the University of Cincinnati, argues that medical educators are rushing to apply their familiar toolkit—policies, competencies, and curricula—to a phenomenon they do not yet fully understand.
On one hand, these responses are necessary. Trainees should develop AI competence, including the ability to critically appraise AI outputs, just as medical education has long taught evidence-based medicine. Still, these responses assume educators know where this is heading and how to get there. They do not.
Rather than trying to predict the future, medical education must become more adaptive. For Turner, this means partnering with learners to co-create new training approaches and embracing newer models such as design-based research and implementation science. For Christy Boscardin, a professor who directs UCSF's AI and Medical Education team, this means helping learners tap into their intrinsic motivation and encouraging them to follow up on the outcomes of their decisions made with or without AI.
A Global Reckoning
The conversation is not limited to the United States. Prof. Anat Gafter-Gvili, the incoming head of Tel Aviv University's medical school and director of the Department of Internal Medicine at Rabin Medical Center, believes the next generation of doctors will need less memorization and more critical thinking. "AI tools will not replace doctors, but they will save us time in certain areas and provide valuable insights," she said. "Students need to learn how to use AI tools properly, and they already know them better than we do."
Gafter-Gvili is also pushing to shorten Israel's lengthy medical training pathway. "Today, the path is medical school, followed by internship, and then residency. By the time you become a specialist, 10 years have passed. We need to find a way to shorten this process and make the training more focused." She noted that Tel Aviv University will have 400 medical students this year, significantly more than in the past, reflecting the state's desire to train more doctors.
The Next Chapter
No one knows exactly what medicine will look like in the years ahead. Physicians have faced similar uncertainty before, and new technologies have repeatedly changed how doctors practice. Yet this moment feels different because AI is forcing a reconsideration of both what physicians do and how they learn.
Kimberly Lomis, a surgeon and vice president of medical education innovations at the American Medical Association, sees opportunity in the disruption. "AI is a disruption we should take advantage of," she said. "This is a chance to realign around our values."
As machines increasingly perform tasks that yesterday's trainees learned by doing, the challenge is deciding what every physician still needs to learn, what no longer matters, and what new capabilities they must develop. The physicians trained today will help determine the answer.