Artificial intelligence for radiotherapy dose prediction: A comprehensive review

Abstract

Patient outcomes are significantly impacted by the effectiveness and quality of radiation treatment planning. Deep learning, a branch of artificial intelligence, is a potent tool for enhancing and automating dose prediction processes. This article provides a comprehensive and critical analysis of deep learning-based dose prediction methods in radiotherapy, with a focus on convolutional neural networks. A comprehensive search throughout Elsevier Scopus (R), Medline, and Web of ScienceTM literature databases was conducted to locate relevant papers published between 2018 and 2024. The use of deep learning methods for dose prediction is thoroughly examined in this paper. Analysis ofthese dose prediction approaches provides valuable insights into the potential of this technology to improve radiation treatment planning, particularly in the critical area ofautomating the dose prediction process. The findings aim to guide future research and facilitate the safe and effective integration of artificial intelligence in clinical workflows. (c) 2025 Published by Elsevier Masson SAS on behalf of Socie<acute accent>te<acute accent> franc,aise de radiothe<acute accent>rapie oncologique (SFRO).