ReviewJournal of the Korean Society of Radiology2026
Generative AI in Radiotherapy Imaging: From Cross-Modality Synthesis to Foundation Model-Based Segmentation.
Review in Journal of the Korean Society of Radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Imaging underpins nearly every stage of radiation therapy, yet the images obtained closest to treatment delivery are often the least reliable. Cone-beam CT is degraded by scatter artifacts and inaccurate Hounsfield unit values, whereas on-treatment MRI is limited by time constraints that preclude standard protocols. Generative artificial intelligence provides new solutions to these limitations. This review examines three areas of direct impact: cross-modality image synthesis, in which diffusion models generate CT-quality images foundation model-based segmentation and target delineation, where large-scale pretrained models generalize across modalities without task-specific retraining and multimodal architectures improve delineation by integrating imaging with clinical text; and generative approaches to post-treatment assessment. Persistent challenges, including image hallucinations, algorithmic biases, and data privacy, must be addressed before these tools can be deployed safely.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.