This recommended practice delineates an architecture, and offers suggestions for enhancing the generalizability of artificial intelligence models in medical imaging, including specifying the following suggestions:--An overview of AI solutions in medical imaging from a generalizability standpoint.--Processes and specifications for data processing.--A means of removing compounding factors to reduce bias across various data sets; specifications for AI modeling that account for small training data, uni/multi-modal data in medical imaging, thereby enabling flexibility for deployment across distinct usage scenarios.--Approaches for continuous learning and federated learning to enable AI models to adapt to local populations, and evaluation metrics from a generalizability perspective.