Artificial intelligence across oncologic theranostics: evidence for patient stratification, dosimetry, and adaptive radiopharmaceutical therapy.

Artificial intelligence (AI) has been studied across radiopharmaceutical therapy (RPT); however, evidence for treatment-changing use remains limited. We conducted a structured narrative review with descriptive mapping of learned models for patient stratification, segmentation, tumor-burden quantification, quantitative preprocessing, dosimetry, toxicity prediction, response assessment, and radiation-safety/logistics support. The mapped set contained 73 direct full journal reports and eight direct conference abstracts, with one additional meeting abstract retained as contextual evidence. Among the full reports, 17 were PSMA-related, 16 SSTR/PRRT, 15 radioiodine, 14 ⁹⁰Y radioembolization, and 11 cross-platform or emerging-target reports; 16 addressed segmentation or quantification, 32 selection, response, prognosis, toxicity, or safety/logistics, and 25 registration, preprocessing, or dosimetry. Fifty-one reports were retrospective, one was an explicitly prospective clinical/technical evaluation, nine were technical, synthetic, or phantom evaluations, and temporal design was unclear or conflicting in 12. Seven reported an external-type held-out evaluation, including four that clearly held out an institution; one reported explicit calibration, one propagated task-level uncertainty, and none evaluated a prospective AI-guided treatment policy. The mapped evidence most directly supports human-reviewed measurement and workflow assistance. An eight-domain RPT evidence-to-decision synthesis organizes quantitative fidelity, reference standards, validation, endpoints, biological linkage, oversight, uncertainty and quality assurance, and decision impact; it is not a validated score or adoption standard.