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Historically, several classification systems have been used for brachytherapy, and they were based on the type of clinical purpose, type of implant and timing of the implant, dose-rate, and type of loading for treatment delivery. However, over the last decades, there have been some major technological advancements, including the introduction of image-guidance and possibility to modulate the dose delivered, which have led several authors (in order to highlight the differences between old technique and new approach) to label it in a different way by replacing “brachytherapy” with “interventional radiotherapy”. Modern interventional procedures involve several key aspects, which contribute to the complexity of implant phase, such as implant type, imaging used during the procedure, and role of multi-disciplinary team in operating room. By assigning scores to these procedural elements, it is possible to classify the procedure’s complexity using a COMIRI classification (COMplexity Index of interventional Radiotherapy Implants). The aim of the COMIRI classification system is to appropriately highlight the need for suitable resources based on the complexity level of different procedures in terms of personnel expertise, equipment availability, and multi-disciplinary teamwork.

This review explores the integration of artificial intelligence (AI) in interventional radiotherapy (IRT), emphasizing its potential to streamline workflows and enhance patient care. Through a systematic analysis of 78 relevant papers spanning from 2002 to 2024, we identified significant advancements in contouring, treatment planning, outcome prediction, and quality assurance. AI-driven approaches offer promise in reducing procedural times, personalizing treatments, and improving treatment outcomes for oncological patients. However, challenges such as clinical validation and quality assurance protocols persist. Nonetheless, AI presents a transformative opportunity to optimize IRT and meet evolving patient needs.