  Artificial intelligence (AI) techniques have significant potential to enable
effective, robust and automated image phenotyping including identification of
subtle patterns. AI-based detection searches the image space to find the
regions of interest based on patterns and features. There is a spectrum of
tumor histologies from benign to malignant that can be identified by AI-based
classification approaches using image features. The extraction of minable
information from images gives way to the field of radiomics and can be explored
via explicit (handcrafted/engineered) and deep radiomics frameworks. Radiomics
analysis has the potential to be utilized as a noninvasive technique for the
accurate characterization of tumors to improve diagnosis and treatment
monitoring. This work reviews AI-based techniques, with a special focus on
oncological PET and PET/CT imaging, for different detection, classification,
and prediction/prognosis tasks. We also discuss needed efforts to enable the
translation of AI techniques to routine clinical workflows, and potential
improvements and complementary techniques such as the use of natural language
processing on electronic health records and neuro-symbolic AI techniques.
