  Artificial intelligence (AI)-based methods are showing promise in multiple
medical-imaging applications. Thus, there is substantial interest in clinical
translation of these methods, requiring in turn, that they be evaluated
rigorously. In this paper, our goal is to lay out a framework for objective
task-based evaluation of AI methods. We will also provide a list of tools
available in the literature to conduct this evaluation. Further, we outline the
important role of physicians in conducting these evaluation studies. The
examples in this paper will be proposed in the context of PET with a focus on
neural-network-based methods. However, the framework is also applicable to
evaluate other medical-imaging modalities and other types of AI methods.
