Giving a clinical team access to an AI-generated treatment suggestion does not automatically shorten the path to a decision; it can lengthen it. Physicians treat an algorithmic recommendation less like a shortcut and more like a second opinion that has to be checked, which triggers its own review cycle. That review means pulling the underlying literature, weighing the patient's specific history against the model's generic pattern-matching, and writing down a rationale thorough enough to survive a later audit.

At one teaching hospital's oncology department, a decision-support tool began flagging suggested regimens alongside each new case. Rather than rubber-stamping the suggestions, the tumor board added an extra ten to twenty minutes per case explicitly debating where the algorithm's reasoning diverged from the patient in front of them. Case conferences ran longer, but junior physicians reported leaving each one with a sharper sense of exactly why a treatment plan was chosen, not just what it was.

The mechanism is straightforward: a recommendation that must be actively defended or overridden forces exactly the kind of deliberate reasoning that builds expertise, while a recommendation that is simply accepted teaches nothing. The paradox is that the more capable and authoritative the AI output looks, the harder clinicians work to justify departing from or confirming it, and that extra friction is where the learning happens. Automation only erodes skill when its output is trusted blindly; when it's treated as a claim to be tested, it does the opposite. That pattern holds outside medicine too, wherever a model's suggestion is treated as contestable rather than final.