Doctors in Training Are Turning to AI, But No One Is Teaching Them How

When sailors in previous centuries steered across open water, they trusted the stars above them — fixed, distant points of light that offered direction even when the ocean offered nothing but uncertainty. A new survey study from the University of Pittsburgh Medical Center borrows that metaphor to describe how today’s doctors-in-training navigate generative artificial intelligence: a bright, alluring reference point, but one whose reliability nobody has fully charted. The study, published in the Journal of General Internal Medicine, examines how residents and faculty physicians in graduate medical education are actually using these tools, and it arrives at a moment when the answer matters more than ever.

The research, led by Dr. Georgia M. Vasilakis of the UPMC Heart & Vascular Institute alongside Dr. Kaitlyn E. McSurdy, Dr. Kathryn Berlacher, and Dr. Natalie Stokes, is a cross-sectional survey study — a snapshot in time of attitudes and behaviors across a single academic medical community. Cross-sectional designs like this one capture prevalence and perception rather than causation, but they are precisely the instrument needed when a technology has spread faster than the literature describing it. Graduate medical education, the period when medical school graduates train as residents and fellows under faculty supervision, is an especially revealing setting, because it pairs the least experienced clinicians with the most experienced ones and asks how both groups respond to the same unfamiliar technology.

The urgency of the question is hard to overstate. Generative AI systems — large language models that can draft prose, summarize evidence, answer clinical questions, and simulate patient encounters — have penetrated professional life at a pace with few precedents in medicine. Surveys of physicians conducted by the American Medical Association in 2026, cited in the study’s bibliography, document rapidly shifting sentiment toward augmented intelligence in clinical practice. Yet formal guidance has lagged well behind adoption. A 2025 report from the Association of American Medical Colleges found that artificial intelligence curricula remain unevenly implemented across U.S. and Canadian medical schools, meaning many trainees arrive in residency having never received structured instruction in what these tools can do, when they fail, or how their outputs should be verified.

That gap between use and instruction is the study’s central concern. The title’s nautical metaphor — sailing by the stars — is deliberate. Generative AI offers trainees a navigational aid: a quick answer on a busy call night, a draft of a difficult patient message, a summary of a dense guideline document. But stars can mislead. Large language models generate text by predicting plausible word sequences, not by verifying truth, and they can produce confident, fluent, entirely fabricated content — the phenomenon known as hallucination. A resident who consults an AI system at three in the morning may be consulting something that sounds authoritative and is wrong in ways that are difficult to detect without exactly the expertise the trainee is still developing.

The study’s authors situate their work within a growing but still thin body of evidence. Previous research by Cervantes and colleagues, published in the Journal of Investigative Medicine in 2024, examined medical educators’ perceptions of generative AI and found a profession divided — intrigued by the technology’s potential, wary of its risks to rigor and to the development of clinical reasoning. A 2025 review in the New England Journal of Medicine by Abdulnour, Gin, and Boscardin went further, proposing educational strategies for the clinical supervision of AI use itself, arguing that supervising how trainees engage with AI should become as routine as supervising how they prescribe medications or interpret electrocardiograms. The Pittsburgh survey adds empirical ground truth to that theoretical framework by asking what is actually happening in one large academic center.

The stakes of the question extend beyond individual competence into the architecture of medical training itself. Residency is built on graduated responsibility: interns perform supervised tasks, seniors direct teams, attendings hold ultimate accountability. Generative AI scrambles this hierarchy in an unfamiliar way, because it gives the most junior member of the team access to something resembling expert-level output without any guarantee of expert-level accuracy. A 2023 randomized vignette study published in JAMA by Jabbour and colleagues measured this directly, testing whether AI assistance improved diagnostic reasoning for hospitalized patients — and found that the benefits were neither automatic nor uniformly distributed, underscoring that how the tool is deployed matters as much as whether it is deployed at all.

There is also a subtler cognitive risk that the study’s framework acknowledges: the phenomenon of cognitive offloading. Research published in the journal Societies in 2025 by Gerlich examined how reliance on AI tools correlates with diminished engagement in critical thinking, a finding with obvious resonance for a profession whose core skill is precisely disciplined, skeptical reasoning. If a trainee learns to accept an AI-generated differential diagnosis without interrogating it, the technology may quietly erode the very habit of mind that residency exists to build. Conversely, if trainees are taught to treat AI output the way they treat a consultant’s recommendation — as an informed opinion to be weighed, checked, and integrated — the technology could sharpen rather than soften clinical judgment. Which outcome prevails depends heavily on the norms that faculty model and programs codify, which is exactly why measuring both groups’ behaviors matters.

The Pittsburgh team’s design reflects that dual focus. By surveying trainees and faculty within graduate medical education at the same institution, the researchers could capture the full supervision chain: the people most likely to reach for these tools and the people responsible for evaluating the work those tools touch. Portions of the work were presented as a poster at the University of Pittsburgh Graduate Medical Education Connect Conference in February 2026 and as a plenary session at the medical center’s Department of Research Day in May 2026, before the full paper was accepted on August 13, 2026 and published online on August 31, 2026. The authors report no funding and no competing interests, and the study involved no patients or protected health information, sparing it the privacy entanglements that often complicate clinical AI research. In a move that strengthens the study’s transparency, the datasets generated and analyzed are publicly available in the Harvard Dataverse repository, allowing other researchers to interrogate the findings independently.

What makes this study resonant beyond academic medicine is the broader economic and social evidence it aligns with. A widely discussed 2023 field experiment by Dell’Acqua, McFowland, Mollick, and colleagues described the performance of AI on knowledge work as a jagged technological frontier: tasks on one side of an invisible boundary are transformed by AI assistance, while superficially similar tasks just beyond it see little benefit or outright harm. Medicine sits squarely astride that frontier. Drafting administrative text and surfacing literature are tasks where generative models excel; nuanced diagnostic reasoning under uncertainty remains stubbornly human. The problem, as the Pittsburgh survey implicitly frames it, is that trainees and faculty may not agree on where the boundary lies — or may not know it exists at all.

The study’s ultimate contribution may be less a single statistic than a reframing of institutional responsibility. Sailing by the stars served generations of navigators well, but only because the stars’ positions were meticulously cataloged, their small motions understood, and their limitations respected. Generative AI in medical training deserves the same treatment: not prohibition, not blind adoption, but rigorous charting of where it helps, where it misleads, and what competencies trainees must retain regardless of how capable the tools become. The Pittsburgh team — clinicians embedded in the very training environment they studied — have supplied one of the careful navigational fixes the field now needs. As residency programs across the country confront trainees already carrying these tools in their pockets, the question the study poses is no longer whether graduate medical education will engage with generative AI, but whether it will do so with a map or by guesswork.

Subject of Research: Generative AI use among graduate medical education trainees and faculty physicians

Article Title: Sailing by the Stars: A Cross-Sectional Survey Study of Generative AI Use Among GME Trainees and Faculty

Article References: Vasilakis, G. M., McSurdy, K. E., Berlacher, K., & Stokes, N. (2026). Sailing by the Stars: A Cross-Sectional Survey Study of Generative AI Use Among GME Trainees and Faculty. Journal of General Internal Medicine. https://doi.org/10.1007/s11606-026-10751-6

Image Credits: AI Generated

DOI: 10.1007/s11606-026-10751-6

Keywords: generative artificial intelligence, graduate medical education, medical education, residency training, survey study, clinical supervision, physicians, large language models, cognitive offloading, academic medicine, Journal of General Internal Medicine, hallucination