Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond

TLDR

Artificial intelligence is increasingly applied in radiotherapy, enabling large‑scale data processing, automated segmentation, synthetic image generation, and outcome prediction across the entire workflow. The authors performed a systematic PubMed and Scopus search of the past four years, identifying 1,824 papers and analyzing 921 to map AI applications across the radiotherapy workflow. The review highlights concerns such as the need for harmonization and the challenges posed by ethical, legal, and skill barriers.

Abstract

In recent decades, artificial intelligence (AI) tools have been applied in many medical fields, opening the possibility of finding novel solutions for managing very complex and multifactorial problems, such as those commonly encountered in radiotherapy (RT). We conducted a PubMed and Scopus search to identify the AI application field in RT limited to the last four years. In total, 1824 original papers were identified, and 921 were analyzed by considering the phase of the RT workflow according to the applied AI approaches. AI permits the processing of large quantities of information, data, and images stored in RT oncology information systems, a process that is not manageable for individuals or groups. AI allows the iterative application of complex tasks in large datasets (e.g., delineating normal tissues or finding optimal planning solutions) and might support the entire community working in the various sectors of RT, as summarized in this overview. AI-based tools are now on the roadmap for RT and have been applied to the entire workflow, mainly for segmentation, the generation of synthetic images, and outcome prediction. Several concerns were raised, including the need for harmonization while overcoming ethical, legal, and skill barriers.