Table 2. .

Learning theories address challenges that form the foundation to improve radiology precision education with AI

Learning theory	Challenge addressed	Description
Behaviorist theory	How can we improve perceptual and diagnostic accuracy of trainees and medical students?	Experience is a gateway between apprenticeship and autonomy, empowering trainees with greater accuracy.17,26 Patient care can improve with increased trainee experience.
Cognitive theory	How can we improve radiology education and its use of textbooks and lectures as the main access points to knowledge and diagnostic thinking?	With growing knowledge requirements in medicine, and definitions of pathology ever-evolving, the knowledge gap between trainee and attending continues to widen. AI can build knowledge efficiently and highlight and correct individual cognitive biases.
Constructivist theory	How can we optimize and maximize the time spent between trainees and their teachers?	One-on-one time at workstations is essential to radiology education environments,17 but this is not always achievable in busy reading rooms, with ever-increasing work-loads.1,23 AI can also automate “learning profiles” (discussed later).24