I keep thinking about the first time a young doctor is alone with a patient.
No supervisor hovering. No WhatsApp group lighting up. Just a stethoscope, a story that doesn’t quite fit the textbook, and that uncomfortable pause where you realise… you have to decide. That pause is medicine. Or at least it used to be.
Lately, that pause is shrinking.
The Quiet Risk Isn’t AI. It’s Obedience
The editorial in BMJ Evidence Based Medicine is careful, polite, academic. But underneath the cautious language is a sharper warning: medicine is drifting from thinking to accepting.
AI doesn’t bully young doctors. It doesn’t shout. It just sounds calm, fluent, certain. And confidence, especially early in training, is intoxicating. You stop asking why because the answer arrives fully dressed.
That’s not efficiency. That’s habit formation.
Medicine was never about retrieving information. It was about holding uncertainty without panicking. AI is brilliant at pattern-matching. Diagnosis, however, is pattern-breaking. The danger begins when students confuse the two.
Deskilling Doesn’t Look Like Failure. It Looks Like Smoothness
Here’s the uncomfortable part. Overreliance doesn’t produce bad doctors overnight. It produces doctors who look fine. Efficient. Up to date. Until something unusual walks in.
Cognitive off-loading sounds harmless. We’ve outsourced navigation to GPS, spelling to autocorrect. But when you outsource reasoning before it fully forms, you blunt it permanently. A trainee who never wrestles with ambiguity won’t suddenly develop that muscle at 35.
And AI’s mistakes are especially dangerous because they’re polite. Hallucinations don’t announce themselves. Bias doesn’t wear a warning label.
Why Training Must Teach Doubt, Not Just Output
One idea from the editorial actually made me pause. Training students on intentionally flawed AI outputs. Forcing them to argue back. To reject confidently delivered nonsense using evidence.
That’s closer to real medicine than most exams.
Grade the reasoning. Not the final answer. Make students explain what they don’t trust and why. Because in the real world, patients don’t care how elegant your tool was. They care whether you noticed what didn’t fit.
A Personal Turn: Watching a New Doctor Step In
My daughter, Maryam Jamal, has just passed her MBBS. Watching that moment was pride mixed with something heavier. Relief, yes. But also awareness.
Young doctors today are stepping into a system flooded with tools their seniors never had. That’s not fair or unfair. It’s just reality. The question is how they use them without letting those tools quietly reshape who they become.
So if I were speaking directly to new doctors like her, I’d say this:
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Use AI after you’ve thought, not before. Make your own differential first. Then check yourself.
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Never outsource first principles. Anatomy, physiology, clinical reasoning. These are non-negotiable.
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Be suspicious of confidence, especially your own.
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Treat AI like a junior assistant. Helpful, fast, occasionally wrong. Never in charge.
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Protect the bedside. Communication, examination, judgement. These don’t scale. And that’s the point.
The Question Medicine Has to Answer Now
This isn’t about banning AI or pretending we can roll the clock back. That ship sailed. It’s about deciding what kind of doctors we’re training.
Because when something goes wrong, it won’t be the algorithm sitting with the family, explaining a choice. It will be a human being. A doctor. Alone with that pause again.
The only question is whether we’re still teaching them how to live inside it.
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