In healthcare, there’s growing concern over doctors becoming less clinically adept as they increasingly rely on AI tools. But what about the trainees – medical students, residents and fellows – who are using these tools before they’ve built their own clinical judgment? The idea of deskilling implies that someone possessed an ability and then lost it. Here, the danger is not just deskilling but never-skilling. Although a doctor who has forgotten how to reason is recoverable, one who never learned how may not be.

OpenEvidence, essentially an AI chatbot for clinicians, has given this concern its most concrete form. About two-thirds of US doctors actively use OpenEvidence, asking about puzzling symptoms, drug interactions, and clinical guidelines, getting responses within seconds, anchored in the latest research. Trainees, unsurprisingly, have also begun to use this AI tool in many of the same ways – but at a far more formative stage.

For example, trainees once asked to build a list of potential diagnoses might struggle and offer an incomplete set, learning what they missed, sometimes painfully. Now, trainees can simply ask OpenEvidence and get a nearly perfect answer, complete with possibilities they might have never considered and none of the embarrassment of having overlooked them. Repeating this answer on the wards may make the trainee look prepared and even impress the supervising doctor.

However, this performance can also conceal the very deficit that training is meant to reveal: that the struggle is the point. Medical training, more than most professions, is an apprenticeship. A student becomes a resident, a resident becomes a fellow, and a fellow becomes an attending – every step shaped by failure, uncertainty and increasing responsibility. With years of repetition and watchful supervision, the habits of clinical........

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