Radiation therapy has always been a discipline of exquisite precision, a practice in which millimeters and milligrays separate cure from complication. Now, according to a comprehensive review published in Nature Reviews Clinical Oncology, artificial intelligence is reshaping nearly every step of how that precision is delivered, from the first contour drawn on a CT scan to the final quality check before a beam fires. The review, authored by Evangelia Katsoulakis and Issam El Naqa of the H. Lee Moffitt Cancer Center and Dartmouth Hitchcock Medical Center, argues that AI has already proven itself in radiation oncology, but that its clinical deployment continues to lag far behind its perceived potential for reasons that are technical, practical, ethical and legal in equal measure.
The historical roots of this transformation run deeper than many patients realize. As early as 1996, researchers were using artificial neural networks to score radiotherapy treatment plans, and by the turn of the millennium similar networks were being used to customize beam orientations and to predict biological outcomes in prostate cancer. What has changed is the scale and sophistication of the tools. Deep learning, a subfield of machine learning in which neural networks learn directly from raw data, has enabled convolutional neural networks to segment organs at risk in head and neck CT images with a fidelity that now rivals, and in some cases exceeds, manual delineation by experienced clinicians.
Auto-contouring is perhaps the most mature application. Commercially available and, in many jurisdictions, regulator-approved tools now outline organs such as the heart, spinal cord and parotid glands automatically, and multi-center evaluations of deep learning CT autosegmentation in the head and neck region have demonstrated clinically acceptable performance. A randomized controlled crossover trial evaluating AI-assisted heart contouring, along with prospective multi-center studies of AI-assisted delineation in postoperative lung cancer radiotherapy, reflect a growing body of clinical evidence rather than mere laboratory promise. Yet the review is careful to note that expert human oversight remains essential across AI-enabled workflows, and that the tendency of users to over-rely on automated recommendations, a phenomenon known as automation bias, is itself a documented risk that clinics must actively manage.
Treatment planning, historically the most labor-intensive stage of the radiotherapy workflow, is being transformed along parallel tracks. Knowledge-based planning systems predict dose-volume histograms for organs at risk and have been extended to fully automated planning across ten different cancer sites. Deep reinforcement learning, in which models learn to make decisions by optimizing actions based on feedback from an environment, has produced proof-of-principle systems for high-dose-rate brachytherapy in cervical cancer and hierarchical frameworks for prostate and head and neck intensity-modulated radiation therapy. In parallel, generative AI approaches are being applied to synthetic computed tomography generation, allowing dose calculations to proceed from MRI scans alone, an advance with particular relevance for MR-guided adaptive radiotherapy, where dosimetric evaluations using cycle-consistent generative adversarial network synthetic CT have shown feasibility.
Adaptive radiotherapy, in which plans are modified during a treatment course to account for anatomical change, represents another arena in which AI is already operational. Magnetic resonance-guided prostate stereotactic body radiation therapy with daily online plan adaptation has been tested in prospective phase 1 trials, and multi-institutional phase 2 trials of ablative five-fraction stereotactic MR-guided on-table adaptive radiotherapy for pancreatic cancer have demonstrated the clinical viability of rapid, AI-assisted replanning. Because breathing and other motions can shift targets by significant margins, the ability to re-contour and re-plan in minutes rather than hours changes what is therapeutically achievable, particularly for tumors near the gastrointestinal tract.
Beyond the machinery of planning and delivery, the review highlights AI’s expanding role in predicting outcomes. Models trained on imaging, dose and clinical data can forecast toxicities such as radiation pneumonitis, xerostomia and bowel toxicity, with recent radiomics and dosiomics machine learning approaches drawing on the prospective multicenter RTOG 0617 and REQUITE trials to predict symptomatic lung injury. Multimodal AI, which integrates imaging, text, genomics and clinical data into unified predictions, is extending this capability further, and transformer-based modeling of patient-reported outcomes is being explored for survival prediction in non-small cell lung cancer. Large language models are even being benchmarked for extracting toxicity data from oncology trial records, while ambient AI scribes are being tested to relieve the documentation burden that contributes to clinician burnout.
The most forward-looking section of the review concerns the convergence of digital twins, foundation models and agentic AI. A digital twin is a computational replica of an individual patient that can be used to simulate treatment scenarios before they are applied, and predictive digital twins have already been explored for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas, as well as for adaptive proton therapy strategies using cone-beam CT. Foundation models, large-scale models trained on broad and diverse datasets that can be adapted to downstream tasks with minimal task-specific training, could serve as the engine of such twins, while agentic AI systems composed of multiple autonomously orchestrated agents could coordinate the complex interdependent steps of a full radiotherapy workflow. Feasibility studies of automating radiotherapy planning with large language model agents suggest this is no longer speculative.
The authors are equally candid about why deployment has stalled. Data leakage, in which information from validation or test data inadvertently informs model training, produces misleadingly optimistic performance estimates. Silent model failure, in which performance deteriorates without warning as patient populations or clinical practices shift, poses a patient-safety hazard that conventional quality assurance was never designed to catch. Dataset biases can propagate into population-level disparities in autocontouring accuracy, and hallucination, the generation of fabricated outputs ungrounded in reality, remains an inherent hazard of generative systems. Explainable AI and uncertainty estimation methods that flag low-confidence predictions for human review are proposed as partial remedies, alongside failure mode and effects analyses of automated tools and quality assurance frameworks specifically designed for AI-based applications, including deep learning approaches that predict gamma passing rates for patient-specific QA.
Regulatory and governance infrastructure is beginning to catch up. A joint ESTRO and AAPM guideline for the development, clinical validation and reporting of AI models in radiation therapy has been published, the US Food and Drug Administration has issued guidance on predetermined change control plans for AI-enabled device software and good machine learning practice principles, and the European Union’s AI Act establishes a broader legal framework. Federated learning, which trains models across institutions without moving patient data, is emerging as a technical answer to privacy constraints, with demonstrated applications in dose-volume parameter prediction and survival modeling for hepatocellular carcinoma. Professional societies are also pressing for structured education, including national workshops on training the next generation of radiation oncologists in AI and centralized databases of AI courses in Europe.
What emerges from the review is a field at an inflection point. Ongoing clinical trials are now evaluating not just the technical performance of AI-enabled applications but their safety, efficacy and genuine clinical value, a shift from proof-of-concept toward accountability. The authors, whose work is supported by the National Institutes of Health and the Department of Defense, argue that the convergence of digital twins, generative AI and foundation models is poised to shape the next era of AI-enabled radiotherapy, one in which treatment could be continuously simulated, personalized and adapted for each patient. But they temper that optimism with a clear-eyed warning: without practical recommendations for responsible adoption, rigorous validation and sustained human oversight, the technologies capable of optimizing the delivery of radiotherapy will remain, in clinical terms, underused. The challenge, in other words, is no longer building the intelligence; it is building the trust.
Subject of Research: Artificial intelligence applications in radiation oncology treatment planning, delivery and outcome prediction
Article Title: Optimizing the delivery of radiotherapy with artificial intelligence
Article References: Katsoulakis, E., & El Naqa, I. (2026). Optimizing the delivery of radiotherapy with artificial intelligence. Nature Reviews Clinical Oncology. https://doi.org/10.1038/s41571-026-01204-4
Image Credits: AI Generated
DOI: 10.1038/s41571-026-01204-4
Keywords: artificial intelligence, radiotherapy, machine learning, deep learning, digital twins, generative AI, auto-contouring, treatment planning, adaptive radiotherapy, foundation models, outcome prediction, clinical deployment
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Tags: adaptive radiotherapyadvancements in radiation therapy precisionAI in radiotherapyAI-driven contouring and imaging analysisArtificial Intelligenceauto-contouringclinical deploymentclinical deployment challenges of AI in radiotherapydeep learningdigital twin technology in cancer treatmentdigital twinsethical and legal considerations in AI-powered radiotherapyfoundation modelsfuture prospects of AI and digital twins in cancer caregenerative AIhistory of neural networks in cancer treatment planningintegration of AI tools in radiation treatment workflowMachine learningoutcome predictionpotential of digital twins for personalized cancer therapyradiotherapyrole of deep learning in radiation oncologytechnological barriers to AI adoption in radiation oncologytreatment planning
