  We highlight emerging uses of artificial intelligence (AI) in the field of
theranostics, focusing on its significant potential to enable routine and
reliable personalization of radiopharmaceutical therapies (RPTs). Personalized
RPTs require patient-individual dosimetry calculations accompanying therapy.
Image-based dosimetry needs: 1) quantitative imaging; 2) co-registration and
organ/tumor identification on serial and multimodality images; 3) determination
of the time-integrated activity; and 4) absorbed dose determination. AI models
that facilitate these steps are reviewed. Additionally we discuss the potential
to exploit biological information from diagnostic and therapeutic molecular
images to derive biomarkers for absorbed dose and outcome prediction, towards
personalization of therapies. We try to motivate the nuclear medicine community
to expand and align efforts into making routine and reliable personalization of
RPTs a reality.
