I wrote a guest editorial in Academic Radiology about AI readiness as a practical educational obligation, rather than an optional informatics side quest.
The piece responds to a study of medical undergraduates and radiology trainees in China, but the issue is broader: enthusiasm and exposure do not equal competence. Radiologists need enough AI literacy to recognize workflow fit, automation bias, governance gaps, and the limits of machine suggestions. Most trainees will not become model developers.
They still need to become safe, skeptical operators who can use AI with confidence and accountability in real clinical environments, worldwide, across diverse resource settings today.