The development of artificial intelligence (AI) methodology is of profound importance and is expected to have major societal impact, especially its effect on medicine. In the past funding cycle, we pioneered the application of deep neural networks (DNN) in various image reconstruction tasks and built a solid understanding and extensive experience of its applications in medical imaging. In this new TR&D, we propose use deep learning (DL) to push the application of AI in medical imaging beyond the traditional image reconstruction problem. The study of novel contrast mechanisms (e.g. new MRI sequence and new PET tracer) is a major frontline of PET/MR innovation. To achieve improved image quality, we will incorporate anatomic image and motion correction in a novel DL-based image reconstruction framework. We will also build our AI model based on the accumulated big imaging data at MGH while also providing a methodology to transfer this knowledge to new studies with few existing data. Our proposed domain adaptation and domain adaptation few shot learning technology will largely address some of the biggest challenges of AI in medical imaging, i.e., limited training data, the generalizability problem, thus enabling AI to be practically disseminated and used in clinical environment. Finally, we propose to estimate the posterior distribution of the reconstructed image. The availability of the uncertainty of reconstruction will open a new window for much more elegant and accurate diagnostic protocols and early treatment response evaluation in precision medicine thus leading to a significant number of new applications for PET/MR.