Table 1.

How radiologists can contribute to AI development process

Step	Definition	How radiologists can contribute
1. Defining use case and the conceptual design	Defining clear, clinically relevant use case to achieving specific outcomes (e.g., increasing the speed, accuracy, and efficiency) and translating it into the conceptual design of the solution	Guiding the developers towards clinically relevant use cases (what problem to focus on) and how their solutions can potentially be used by radiologists [6]
2. Data sourcing and curation	Collecting, selecting, cleaning, and organizing the data that is needed for the training and validation of the algorithm	Sharing their data (images and scans), thus being the connection between the available data in the medical world and the AI vendor [8]
3. Labeling and establishing the ground truth	Defining the ground truth and (in case of supervised learning) labeling data	Radiologists as domain experts act on labeling the medical data and are consulted for establishing the ground truth [8, 9]
4. Training the algorithm	Configuring the algorithm (e.g., setting the parameters) and training it	[No specific role of radiologists is currently suggested in the literature]
5. Testing and validating the AI application	Using appropriate and dedicated (reference) datasets to validate trained algorithms and ensure their accuracy and generalizability to clinical cases	Checking the results of the algorithm to be accurate and stable and radiologists can trust them [10, 11]