Artificial intelligence (AI) and informatics are rapidly gaining clinical relevance in healthcare and especially in medical imaging, with 76% of the currently FDA approved AI-enabled medical devices operating in the domain of radiology [1]. In radiology, facilitated by larger numbers of examinations and professionals involved, the significance of AI is also reflected by an ever-increasing number of papers in the scientific literature as well as available teaching sessions with related accreditation at conferences and from societies. In nuclear medicine, however, there is still much to be gained in terms of the development, validation and adoption of AI applications. In addition, the role of informatics translating into dedicated software packages (EPD and PACS) warrants further attention regarding challenges and opportunities. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT) and their accompanying computed tomography (CT) or magnetic resonance imaging (MRI) hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify and integrate these datasets is rather limited, and there would be much be gained from AI-assisted interpretation workflows. Also, identification of novel imaging biomarkers by application of machine learning (ML) algorithms, including deep learning (DL), has the potential to further improve diagnosis, therapy monitoring and prediction of the trajectory of various diseases. Finally, in addition to the potential impact of artificial intelligence for multidisciplinary patient care in nuclear medicine, also research, teaching and training might benefit from its potential.