{"version":1,"type":"story","url":"https://digestai.news/story/ai-transforms-radiotherapy-workflows-while-clinical-adoption-lags","json":"https://digestai.news/story/ai-transforms-radiotherapy-workflows-while-clinical-adoption-lags.json","markdown":"https://digestai.news/story/ai-transforms-radiotherapy-workflows-while-clinical-adoption-lags.md","slug":"ai-transforms-radiotherapy-workflows-while-clinical-adoption-lags","headline":"AI transforms radiotherapy workflows while clinical adoption lags","summary":"Artificial intelligence is reshaping radiation oncology by automating routine tasks such as auto‑contouring, dose calculation, quality assurance and outcome prediction. A growing portfolio of commercially available and FDA‑approved tools is already in use, and many clinical trials are evaluating their technical performance and safety.\n\nDespite these advances, widespread clinical deployment remains limited. Technical integration challenges, ethical and legal concerns, data‑bias risks and the need for expert oversight are repeatedly cited as barriers. Researchers highlight that emerging technologies—generative AI, foundation models, digital twins and agentic AI—could enable more personalized, adaptive treatment planning, potentially defining the next era of radiotherapy.\n\nThe review stresses that responsible adoption will require robust validation, transparent governance and clear regulatory pathways to translate AI’s promise into routine patient care.","keyPoints":["AI automates contouring, dose calculation, quality assurance and outcome prediction, with many FDA‑approved tools now available.","Clinical rollout is slowed by technical, ethical, legal and data‑bias concerns despite ongoing trials.","Emerging generative AI, foundation models, digital twins and agentic AI promise next‑generation radiotherapy planning."],"whyItMatters":"Improving radiotherapy with AI can increase treatment precision, reduce clinician workload and accelerate outcomes, but unresolved safety and regulatory issues may limit patient benefit.","category":{"slug":"research","name":"Research","url":"https://digestai.news/category/research"},"entities":{"companies":["Abbvie","Bristol-Myers Squibb","Pfizer","iRAI Technologies"],"models":[],"people":["E. Katsoulakis","I. El Naqa","E. K."]},"firstPublishedAt":"2026-09-24T00:00:00Z","updatedAt":"2026-09-24T00:00:00Z","sourceCount":1,"hasPrimarySource":true,"sources":[{"outlet":"Nature Machine Learning","title":"Optimizing the delivery of radiotherapy with artificial intelligence","url":"https://nature.com/articles/s41571-026-01204-4","publishedAt":"2026-09-24T00:00:00Z","type":"primary","primary":true,"lead":true}],"sourceNotes":null,"discussions":[],"thread":null,"cite":{"text":"Digest AI, \"AI transforms radiotherapy workflows while clinical adoption lags\", 24 September 2026, https://digestai.news/story/ai-transforms-radiotherapy-workflows-while-clinical-adoption-lags","publisher":"Digest AI","title":"AI transforms radiotherapy workflows while clinical adoption lags","datePublished":"2026-09-24T00:00:00Z","url":"https://digestai.news/story/ai-transforms-radiotherapy-workflows-while-clinical-adoption-lags"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}