Agentic Artificial Intelligence in Medical Imaging Education: Architectural Autonomy and the Risk of Cognitive Surrender

As agentic artificial intelligence systems become increasingly embedded in medical imaging, practice is moving from episodic decision support to workflow-based architectures that alter how practitioners think and practise. Medical imaging practice is traditionally conceptualised using Dual Process Theory, which describes how practitioners use their System 1 (intuitive decision making) and System 2 (analytic decision making) in practice. However, as more practitioners incorporate agentic artificial intelligence systems into their workflow, a Tri-System framework may be required. This Perspective paper will show how the practitioner and an agentic artificial intelligence system become part of a cognitive team known as System 3. It will argue that an appropriate level of cognitive surrender should be considered and that current decision making should be reframed through diagnostic complementarity, with added emphasis on structured human and AI interaction to achieve optimal performance. We recommend the implementation of the following educational methods in radiography programmes: (a) training students using fault-injected medical images to reinforce the importance of human verification in image interpretation; (b) preparing students to supervise the performance of agentic artificial intelligence systems; (c) normalising AI-assisted activities to mitigate potential deskilling.