Artificial Intelligence Across the Cancer Theranostics Workflow: Critical Appraisal of Current Evidence and Future Clinical Translation

Abstract Artificial intelligence (AI) is transforming cancer management and theranostics by improving the accuracy, efficiency, and personalization of diagnostic and therapeutic workflows. Routine and accurate clinical implementation of theranostics remains limited by complex dosimetry procedures, demanding imaging protocols, and challenges in quantitative image analysis. This review critically evaluates the role of AI throughout the cancer theranostic workflow, with a focus on its potential to address the key clinical and technical challenges that continue to limit the routine implementation of personalized radiopharmaceutical therapy. It examines the current maturity of AI applications, their readiness for clinical translation, and the future prospects. Recent advances in machine learning and deep learning have enabled automated image interpretation, enhanced quantitative imaging, accelerated acquisition protocols, single-time-point dosimetry, and supported radiomics and multi-omics analyses. Emerging concepts, such as theranostic digital twins, physics- and biology-informed neural networks, and explainable AI are also discussed as future directions for precision medicine. Despite substantial progress, challenges related to data quality, interpretability, ethics, privacy, standardization, and clinical validation continue to hinder widespread clinical adoption. Nevertheless, AI-driven technologies are expected to play a central role in advancing personalized radiopharmaceutical therapy and facilitating routine dosimetry-guided treatment in clinical practice.