{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "review article",
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "name": "Sangjoon Park",
      "orcid": "0000-0002-9223-3172"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9hsea8sxc7h2h.bib",
  "cited_by": 0,
  "doi": "10.3348/jksr.2026.0084",
  "fulltext_url": "https://europepmc.org/articles/PMC13680131",
  "id": "9hsea8sxc7h2h",
  "issns": [
    "2951-0805"
  ],
  "landing_url": "https://europepmc.org/article/MED/42903063",
  "language": "eng",
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7217522913",
  "pdf_url": "https://europepmc.org/articles/PMC13680131?pdf=render",
  "pmcid": "PMC13680131",
  "pmid": "42903063",
  "published": "2026-09-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9hsea8sxc7h2h.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:57Z",
      "native_id": "MED:42903063",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42903063"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Generative AI in Radiotherapy Imaging: From Cross-Modality Synthesis to Foundation Model-Based Segmentation.",
  "title_text": "Generative AI in Radiotherapy Imaging: From Cross-Modality Synthesis to Foundation Model-Based Segmentation.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:57Z",
  "url": "https://research.draconitic.com/paper/9hsea8sxc7h2h",
  "venue": "Journal of the Korean Society of Radiology",
  "versions": []
}