How might the increasing use of artificial intelligence in medical diagnosis and treatment planning affect the doctor patient relationship and the development of clinical intuition in young physicians

What’s likely to change: AI will shift some diagnostic and planning tasks from unaided human judgment to algorithmic suggestions. That can improve accuracy and consistency, but it also changes workflows, authority, and the everyday experiences from which clinicians build intuition.

Why it matters: clinical intuition develops by pattern recognition, feedback on decision outcomes, and apprenticeship. If AI routinely supplies diagnoses or narrows differential lists, trainees get fewer raw cases and weaker error signals, so their tacit knowledge and calibrated judgment may atrophy. For patients, reliance on opaque algorithms can erode trust in clinicians (who appear to defer to a “black box”) or create misplaced confidence when AI is wrong.

Net effect depends on design and governance: AI used as an explainable assistant that preserves trainee reasoning and provides corrective feedback can augment learning; opaque, authoritative tools promote deskilling and blunt the therapeutic relationship. Ethical, educational, and regulatory choices will therefore shape whether AI strengthens or weakens doctor–patient bonds and clinicians’ intuition.

Do you want examples of specific training or policy changes that keep AI from undermining clinical intuition?