Table 1 Learning goals distilled based on the institutional goals and the results of the pre-curriculum survey

1

Follow fundamental architecture, structure, and implementation of AI systems to gain hands-on experience

This includes gaining experience on the following:

a) The utility of AI in screening and triage

b) The role of AI in precision medicine

c) The limitations of the use AI in clinical practice

2

Understand how AI and machine learning approaches can be exploited for clinical research and associated shortcomings

These include recognition of the following:

a) The significance of image quality and number of observations for effective AI application

b) Consequences of using multi-institutional/multi-scanner data for research

c) Overfitting, superfluous results, and their consequences

3

Broadly understand types of existing major open-source and commercial platforms that are already in use in clinical practice

This includes understanding and explain the following:

a) The types and capabilities of commercially available AI systems and systems based on machine learning approaches

b) How these can offer diagnostic support, such as image classification/detection, image segmentation, image registration, anomaly detection, and cross modality synthesis

c) How they can integrate these systems into their clinical workflow to reduce burden on radiologists