Brosch-Lenz, JuliaYousefirizi, FereshtehZukotynski, KatherineBeauregard, Jean-MathieuGaudet, Vincent C.Saboury, BabakRahmim, ArmanUribe, Carlos F.2023-11-212023-11-212021-10https://doi.org/10.1016/j.cpet.2021.06.002http://hdl.handle.net/10012/20109The final publication is available at Elsevier via https://doi.org/10.1016/j.cpet.2021.06.002. © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/KEY POINTS AI has shown promising applications in quantitative imaging required for dosimetry. Segmentation of organs and tumors, the most time consuming task in the dosimetry workflow, can be automated using AI. Using the theranostic approach, AI models that predict absorbed dose and therapy outcomes might play a key role in personalizing RPTs. AI has significant potential to improve accuracy and reduce times for routine implementation of patient-specific dosimetry in RPTs.entheranosticsradiopharmaceutical therapiesdosimetryartificial intelligenceoutcome predictionsegmentationregistrationquantitative imagingArticle