Radiation oncology generates vast amounts of data at every step of care—from simulation and contouring to planning, delivery, and follow‑up—creating fertile ground for artificial‑intelligence tools that can shorten workflows, standardize decisions, and link treatment to outcomes. We performed a PRISMA‑guided systematic review of the literature (PubMed, Embase, Scopus, Web of Science, IEEE Xplore, and arXiv; January 2000–July 2025) to identify studies that applied machine‑ or deep‑learning methods to segmentation, treatment‑planning dose prediction, synthetic CT or CBCT enhancement, quality assurance, motion tracking, radiomics‑based prognosis, or adaptive radiotherapy. After dual‑reviewer screening of 33 records, 18 studies met inclusion criteria for the core synthesis and 15 were retained as contextual background. The most robust evidence—and the greatest external validation—was found for supervised auto‑segmentation: one multi‑institutional NSCLC study included more than 2,000 patients, and a re‑analysis of RTOG 0617 showed that deep‑learning heart contours altered mean heart dose and strengthened dose‑survival associations. Deep‑learning dose‑prediction and autoplanning workflows achieved plan quality comparable to expert planners while markedly reducing planning time. Synthetic CT and CBCT correction improved dose calculation and image registration in adaptive workflows, and predictive quality‑assurance models showed promising sensitivity and specificity. Radiomics studies frequently reported high internal performance but seldom provided external validation or calibration. Overall, artificial intelligence is already clinically useful for auto‑segmentation and planning assistance; however, broad deployment will require multi‑center external validation, systematic calibration, drift monitoring, and outcome‑linked pragmatic trials embedded within a learning‑health‑system framework.
Ayachi et al. (Thu,) studied this question.