Patient-specific dosimetry is currently a clinical need to evaluate lesion and organs at risk evolution in radiopharmaceutical therapy (RPT). Conventional dosimetry protocols are often time and/or computationally intensive, which dampers the applicability or real personalized dosimetry. Deep learning solutions for time-integrated activity to dose conversion present alternatives to costly Monte Carlo simulations while not relying on generic anthropomorphic models that are agnostic of the patient's anatomy. Artificial intelligence-enabled segmentation strategies support the evolution of personalized, image-guided RPT planning and monitoring. Quantification of radiopharmaceutical uptake and response at the lesion level enable clinicians to assess therapeutic efficacy and adapt treatment accordingly.