Integration of Artificial Intelligence in Radiation Oncology: A Narrative Review

Introduction

The integration of artificial intelligence (AI) into radiation oncology represents one of the most promising advancements in modern oncologic care. AI refers to computer systems designed to perform tasks that typically require human intelligence, such as learning, reasoning, problem-solving, and understanding language. In the medical field, AI encompasses a range of technologies, including machine learning, natural language processing (NLP), and robotics, with the ultimate goal of enhancing patient care and healthcare operations. With the rapid growth of computational power, deep learning algorithms, and predictive analytics, AI is poised to revolutionize the entire radiation therapy workflow—from initial patient consultation to long-term survivorship care. By leveraging AI-driven technologies, radiation oncology can enhance efficiency, precision, and personalization, ultimately leading to improved patient outcomes and optimized resource utilization, see Figure 1.

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Fig. 1.

Enhanced AI Workflow in Radiation Oncology. AI, artificial intelligence.

As cancer incidence continues to rise globally, the demand for radiation therapy is projected to exceed the capacity of the existing workforce. ¹ AI offers scalable solutions that can alleviate the increasing burden on healthcare systems by automating complex processes such as tumor segmentation, treatment planning, quality assurance (QA), and patient monitoring. AI-driven decision support tools are already demonstrating their ability to refine clinical workflows, reducing physician workload and improving treatment accuracy and reproducibility. However, alongside the enthusiasm for AI’s potential, critical challenges must be addressed to ensure its safe and effective integration into clinical practice. These include regulatory considerations, ethical implications, workforce adaptation, and the need for robust validation across diverse populations and clinical settings.

Radiation oncology, a technologically-dependent specialty, continually transforms in parallel with advances in planning software and modern treatment delivery platforms. Notable examples over the last two decades include image-guided radiation therapy (IGRT), adaptive radiotherapy, and stereotactic body radiation therapy (SBRT) allowing for unparalleled precision in tumor targeting. ² With these advances comes an increasing demand for accuracy, efficiency, and real-time decision-making, pushing the limits of human capability. Early radiation plans created by delineating treatment fields with markers on X-ray films have evolved into intricate anatomy-based intensity-modulated plans with subcentimeter accuracy. Improvements in treatment accuracy conferring improved therapeutic ratio require increased time spent on each treatment plan. AI-driven automation and decision support tools can address this complexity by enhancing consistency in tumor delineation, dose optimization, and real-time image analysis, ensuring safer and more effective treatment delivery. ³

According to the World Health Organization (2024), global cancer cases are expected to rise by 77% by 2050, reaching an estimated 35 million new cases per year. ⁴ The growing global need for radiation therapy is underscored by declining number of trained radiation oncologists, medical physicists, and dosimetrists. ⁵ The burden of manual contouring, treatment planning, and administrative documentation diverts physician attention from patient-centered care. In addition, many radiation oncologists perform these asynchronous tasks after hours and on weekends, impacting their quality of life and contributing to physician burnout. ⁶ Thus, scalable technological solutions are warranted to address the pending discrepancy in radiation therapy supply and demand. AI-driven automation offers a compelling solution: by reducing time-consuming, repetitive tasks, AI can free up clinicians to focus on complex decision-making and direct patient interaction. In addition, AI-powered workflow optimization can enhance clinical efficiency, allowing practices to see more patients without compromising quality.

While AI’s capabilities are advancing rapidly, its clinical implementation must be approached with careful oversight. The use of AI in radiation oncology necessitates rigorous validation, regulatory approval, and ethical safeguards to ensure patient safety. Regulatory agencies, including the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA), are actively developing AI-specific guidelines to balance innovation with patient protection. ⁷ Furthermore, considerations around data privacy, algorithmic bias, and the appropriate role of AI in medical decision-making remain at the forefront of AI policy discussions. ⁸ Table 1 lists some currently available AI tools in radiation oncology, where they can be applied, and the strengths and weaknesses of each.

AI in Patient Access and Experience

One of the most profound contributions of AI to modern oncology is its ability to improve patient access to radiation therapy. Geographic disparities, long wait times, and inefficient referral systems often create barriers to timely treatment. AI-powered telemedicine platforms and predictive analytics are addressing these challenges by optimizing appointment scheduling, triage processes, and virtual consultation services. For example, Memorial Sloan Kettering Cancer Center ([MSKCC], USA) has implemented AI-enhanced scheduling algorithms that dynamically adjust physician availability and appointment prioritization, reducing patient wait times and maximizing resource allocation. Similarly, the NHS AI Lab (U.K.) is leveraging AI-powered equity-driven outreach programs to identify underserved populations, ensuring that high-risk patients receive timely radiation therapy referrals. These measures also reduce the administrative burden of managing patient and provider schedules. ⁹

AI-powered triage systems can analyze patient symptoms, medical history, and imaging data to optimize referral pathways, ensuring that patients are directed to the most appropriate specialists without unnecessary delays. AI-assisted virtual consultations, which integrate machine learning algorithms with physician decision-making, are proving to be effective tools for remote patient assessment, particularly in regions with limited access to radiation oncologists. ¹⁰

Beyond improving access to care, AI is also transforming the patient experience by providing personalized, on-demand support throughout the radiation therapy journey. AI-driven conversational agents (chatbots) and NLP-based educational tools are empowering patients with evidence-based information, treatment guidance, and symptom management recommendations. ¹¹ For instance, the Mayo Clinic’s AI-driven patient engagement platform leverages machine learning algorithms to deliver customized patient education materials, ensuring that individuals receive relevant, easy-to-understand information about their diagnosis and treatment options. ¹² In addition, AI-powered symptom monitoring platforms allow patients to self-report side effects in real-time, enabling early intervention and personalized supportive care. ¹³

The adoption of AI into treatment planning is moving radiation oncology toward a more automated, precise, and adaptive paradigm. By leveraging machine learning, real-time imaging analysis, and AI-driven dose optimization, clinicians can achieve higher levels of accuracy, efficiency, and patient personalization than ever before. The next frontier in AI-driven patient care is the development of integrated patient navigation systems that seamlessly coordinate appointments, symptom tracking, treatment adjustments, and survivorship planning. AI’s ability to analyze complex datasets and predict individual patient needs holds tremendous promise for improving the continuity of care and fostering more personalized, patient-centered oncology services.

Despite these advancements, significant challenges must be addressed to ensure responsible and effective AI integration. Interoperability issues remain a major hurdle, as AI models must seamlessly integrate with diverse electronic health record (EHR) systems, imaging platforms, and treatment planning software. Without standardized protocols and cross-platform compatibility, inefficiencies and data silos could limit the full potential of AI in streamlining clinical workflows. Moreover, the potential for automation bias, where clinicians may over-rely on AI recommendations without critically assessing their validity, poses a serious risk. AI-generated treatment plans, while highly optimized, may not always account for nuanced clinical judgments or patient-specific complexities. Ensuring that AI remains a decision-support tool rather than a decision-maker will be essential in preventing unintended errors and maintaining physician oversight.

Another pressing concern is the increasing dependence on “black box” models, where AI-driven decisions are based on complex, opaque algorithms that lack clear interpretability. If clinicians cannot fully understand or explain how an AI system arrives at a particular treatment recommendation, it may undermine trust in AI-assisted planning and limit adoption. Developing more explainable AI models will be crucial to ensuring transparency, fostering clinician confidence, and facilitating regulatory approval. Ensuring responsible AI integration, with appropriate regulatory oversight and human involvement, will be crucial to maintaining high standards of patient safety and clinical excellence. As AI continues to evolve and gain acceptance in clinical practice, addressing these challenges will be critical to maximizing its potential while safeguarding patient care. AI’s role in treatment planning, real-time adaptation, and decision support will likely become indispensable as the field of radiation oncology advances—but only if implemented with rigorous validation, clinician engagement, and robust ethical considerations.

AI in Medical Records and Clinical Decision Support

The digitization of healthcare records was intended to streamline clinical workflows and improve data accessibility, yet in practice, EHRs remain a significant bottleneck in modern oncology care. ¹⁴ Radiation oncologists must navigate fragmented data systems, inconsistent documentation formats, and overwhelming volumes of patient information, often leading to delays in clinical decision-making and increased administrative burden.

One of the primary challenges is data silos—EHR platforms from different institutions frequently lack interoperability, making it difficult to aggregate, share, and interpret patient data across healthcare networks. This is particularly problematic in radiation oncology, where treatment planning requires comprehensive access to diagnostic imaging, pathology reports, prior treatments, and multidisciplinary care notes. ¹⁵ In addition, the time-intensive nature of manual chart reviews further exacerbates clinician workload, reducing the time available for patient interactions and complex decision-making. AI offers a compelling solution to these challenges through machine learning-driven automation and NLP. ¹⁶ These technologies can analyze, extract, and summarize vast amounts of clinical data in real time, improving efficiency, accuracy, and accessibility. AI-powered data integration platforms can bridge the gaps between disparate EHR systems, facilitating seamless data sharing and enhanced multidisciplinary collaboration.

One of the most promising applications of AI in oncology is NLP, which allows computers to interpret and structure unstructured clinical text from EHRs. ¹⁵ NLP models can automatically summarize patient histories, identify key risk factors, and highlight clinically relevant information, reducing the burden of manual data extraction for physicians. ¹⁷ These capabilities are particularly useful in radiation oncology, where treatment planning depends on precise, time-sensitive data aggregation from multiple sources. AI-enhanced clinical decision support systems leverage predictive analytics and real-world data insights to help oncologists determine optimal treatment strategies. By analyzing thousands of patient cases and treatment outcomes, AI can provide evidence-based recommendations tailored to individual patients, reducing uncertainty in complex clinical scenarios. ¹⁸

One of the most well-known applications of AI in oncology, International Business Machines, Inc. (IBM) Watson for Oncology utilizes NLP and machine learning to analyze peer-reviewed literature, clinical guidelines, and patient-specific data to generate personalized, evidence-based treatment recommendations. ¹⁹ While its effectiveness has been met with mixed results in real-world trials, it has demonstrated the feasibility of AI-driven clinical decision-making. ²⁰ Flatiron Health is another AI-powered oncology data analytics platform designed to aggregate and analyze real-world oncology data, assisting clinicians in treatment planning and outcomes tracking. ²¹ Flatiron Health has successfully integrated AI into clinical research and retrospective data analysis, improving the generalizability of real-world evidence for radiation oncology. ²² AI’s role in medical records and decision support is still evolving, yet early applications indicate substantial promise in improving workflow efficiency, treatment accuracy, and data-driven decision-making. ¹⁹

AI in Consultation, Diagnosis, and Tumor Delineation

AI is significantly enhancing clinical decision-making in radiation oncology by actively participating in consultation and diagnostic workflows. Radiation oncologists utilize AI-driven risk stratification models, tumor progression predictions, and multidisciplinary tumor board (MTB) discussions to develop individualized treatment plans. Machine learning models trained on extensive cancer datasets can now predict tumor progression, treatment responses, and patient prognoses with remarkable accuracy. For instance, AI-assisted clinical decision-making frameworks have been developed to assist oncologists in determining optimal dose prescriptions, thereby increasing the efficiency and quality of radiation therapy.^23,24

AI-based risk stratification tools analyze clinical variables, imaging data, and genomic information to classify patients into low-risk, intermediate-risk, or high-risk categories, guiding personalized treatment recommendations. These tools have demonstrated potential in improving patient selection for radiation therapy, refining dose prescriptions, and enabling adaptive treatment strategies. ²⁵

MTBs are essential in comprehensive oncology treatment planning, bringing together various specialists to discuss complex cases. However, the increasing volume of patient data and imaging studies can make MTB discussions time-consuming and cognitively demanding. AI-powered decision support systems can assist MTBs by aggregating and summarizing key clinical data from EHRs, highlighting relevant clinical guidelines, and generating AI-assisted treatment recommendations based on similar patient cases and outcomes. Early implementations of AI-supported MTBs have shown promise in reducing meeting times, improving decision consistency, and enhancing guideline adherence. ²⁶

One transformative application of AI in radiation oncology is its role in medical imaging analysis and tumor delineation. Traditional tumor contouring and segmentation rely on manual delineation by radiation oncologists, a process that is time-intensive and prone to variability. AI is revolutionizing this process by providing automated, highly precise segmentation tools. Deep learning algorithms, particularly convolutional neural networks, have demonstrated high accuracy in tumor segmentation across various cancer types. AI-based auto-segmentation models can delineate tumors and critical structures rapidly, reducing manual workload, improving consistency, identifying deviations in contouring and plan creation warranting further peer review, and adapting in real-time for adaptive radiotherapy workflows. ²⁷

Beyond segmentation, AI is unlocking the potential of radiomics—the extraction of quantitative imaging biomarkers from medical images. AI-powered radiomics models can identify subtle imaging features not visible to the human eye, correlating them with tumor biology, treatment response, and patient prognosis. For example, AI-driven radiomics models have been developed to predict tumor progression and radiosensitivity, aiding in precision oncology approaches. ²⁸ With continued validation and refinement, AI is poised to become an integral component of diagnostic and treatment planning workflows in radiation oncology. The ability to automate tumor segmentation, predict treatment responses, and provide real-time decision support has the potential to enhance both clinical efficiency and patient outcomes.

AI in Treatment Simulation

Treatment simulation is a critical first step in radiation oncology, serving as the foundation for accurate dose delivery, tumor targeting, and patient positioning. ²⁹ Traditionally, this process relies on computed tomography (CT) or magnetic resonance imaging (MRI) scans acquired in a fixed immobilization setup, ensuring reproducibility throughout the course of treatment. However, intrafraction and interfraction variations, such as tumor motion, anatomical shifts, and patient movement, pose significant challenges to treatment accuracy. AI-driven approaches are now enhancing motion tracking, real-time tumor localization, and adaptive simulation workflows, leading to greater precision and efficiency.^30,31

Managing tumor motion presents a significant challenge in radiation therapy simulation, especially for thoracic, abdominal, and head-and-neck malignancies, where physiological activities such as respiration, digestion, and muscular movements can alter tumor positioning during imaging and treatment. AI-powered motion tracking systems are transforming this aspect of treatment planning by predicting tumor motion patterns using deep learning algorithms trained on historical imaging data. These systems integrate real-time imaging modalities, including four-dimensional CT (4D CT), fluoroscopic imaging, and magnetic resonance linear accelerators (MR-Linac), to adjust for tumor displacement. In addition, AI-driven gating systems enable automated synchronization of beam delivery with the tumor’s motion trajectory, enhancing the precision of radiation therapy. For instance, the CyberKnife system utilizes stereoscopic X-ray imaging to detect implanted markers, correlating them with external surrogates to track tumor motion accurately. ³²

Deep learning models have demonstrated the ability to predict tumor motion with sub-millimeter accuracy, significantly reducing geometric uncertainties in treatment planning. These innovations hold the potential to minimize radiation exposure to surrounding healthy tissues while ensuring that tumors receive optimal therapeutic doses. Adaptive radiation therapy (ART) represents a paradigm shift in simulation, allowing treatment plans to be adjusted dynamically in response to anatomical or physiological changes throughout a patient’s treatment course. ³¹ Traditionally, re-simulation and re-planning in radiation therapy required manual intervention, leading to delays and increased workloads for clinicians. AI-powered ART solutions are now transforming this process by automating re-simulation decisions based on daily imaging changes. These systems utilize AI-driven deformable image registration to assess anatomical variations over time, enabling precise adjustments to treatment plans. In addition, AI models can predict dose discrepancies resulting from factors such as weight loss, tumor shrinkage, or positional changes, thereby enhancing treatment personalization and accuracy. For instance, deep learning-based adaptive dose prediction models have been developed to fine-tune initial planning data, facilitating rapid evaluation of dosimetric changes and supporting efficient re-optimization during adaptive radiotherapy sessions. These adaptive workflows enable real-time modification of treatment plans, ensuring that each radiation dose fraction is optimized to reflect the patient’s most current anatomy. ³¹

One of the most significant advances in AI-driven treatment simulation comes from the Princess Margaret Cancer Centre in Toronto, Canada, which has developed a deep learning-based adaptive radiotherapy framework that integrates AI-driven anatomical modeling with real-time imaging data. Their research has demonstrated improved accuracy in motion-adaptive treatments, particularly in lung and gastrointestinal cancers, where tumor movement presents a significant challenge. ³³

By integrating AI into the simulation and planning process, radiation oncology is advancing toward a future in which treatment strategies are continuously refined to maximize precision, reduce toxicity, and enhance patient outcomes.

AI in Radiation Treatment Planning

Radiation treatment planning is one of the most labor-intensive and complex steps in the radiation oncology workflow, requiring precise target delineation, development of beam configurations, and calculations of radiation dose distribution. Traditionally, this process depends on human expertise, often taking several hours to days to optimize a personalized treatment plan. AI is now streamlining dosimetry, beam arrangement, and real-time adaptive planning, significantly reducing planning time while enhancing accuracy and reproducibility.

One of AI’s most impactful contributions to treatment planning is automated contouring, which involves delineating tumors, organs at risk (OARs), and critical structures on medical images. Deep learning-based segmentation models have demonstrated expert-level accuracy in auto-contouring, significantly reducing inter-physician variability and time spent on manual delineation. ³⁴ AI has emerged as a transformative tool in radiation oncology, particularly in the domain of auto-contouring for treatment planning. AI-driven contouring systems automatically identify tumors and OARs across various imaging modalities, including CT, MRI, and positron emission tomography scans. This automation enhances consistency in radiation planning by minimizing inter-user variability, thereby standardizing the delineation process. Moreover, the integration of AI-based auto-contouring into clinical workflows has significantly reduced planning times from hours to mere minutes, facilitating faster initiation of treatment. For instance, studies have demonstrated that AI-powered software can generate reliable and consistent contours rapidly, leading to substantial time savings and improved workflow efficiency. ³⁵

The adoption of AI-based auto-contouring solutions has yielded substantial gains in efficiency for radiation oncology departments while upholding high standards of accuracy. Clinically validated deep learning segmentation tools have been shown to contour scans in seconds, utilizing preferred structure set templates from planning systems. These advancements not only streamline the treatment planning process but also ensure that patient data remains local and secure, as state-of-the-art deep learning is performed entirely on local computers without the need for cloud transfer. Furthermore, AI-based auto-contouring has been instrumental in reducing the workload of radiotherapy staff and standardizing key steps in CT simulation. By automating the contouring of OARs, these solutions provide consistent results that serve as a reliable starting point for treatment planning. This consistency is crucial, as contouring errors have been identified as a high-risk aspect of the radiotherapy process, and interobserver variability can lead to uncertainties in treatment outcomes. ³⁶ In summary, the integration of AI-driven auto-contouring into radiation oncology practices enhances the precision and efficiency of treatment planning. By automating the identification of tumors and OARs, improving consistency, and reducing planning times, AI contributes to improved patient outcomes and optimized resource utilization within clinical settings.

In radiation oncology, the meticulous design of treatment plans is paramount, requiring radiation oncologists, medical dosimetrists, and physicists to determine optimal beam angles, energy levels, and dose distributions tailored to each patient. AI is revolutionizing this process by enabling data-driven, automated plan optimization. Machine learning algorithms can predict optimal beam arrangements based on historical treatment data, optimize dose calculations to maximize tumor coverage while minimizing toxicity, and utilize reinforcement learning to dynamically refine dose constraints. These AI-driven models have demonstrated superior performance over traditional planning approaches, especially in complex cases necessitating highly conformal radiation therapy techniques. ³⁷

The future of radiation oncology is moving towards real-time adaptive therapy, where treatment plans are modified dynamically in response to daily anatomical changes. AI-based adaptive planning facilitates automated adjustments to treatment plans based on patient positioning variations, integrates real-time imaging modalities such as MRI or cone-beam CT for daily dose modifications, and employs predictive modeling of dose-response relationships to enhance treatment personalization. This approach allows for treatments to be tailored in real-time, accommodating individual patient anatomy and changes observed during therapy. ³⁸

Varian’s Ethos radiation therapy system is one of the most advanced AI-driven adaptive radiotherapy solutions, utilizing machine learning to dynamically adjust treatment plans based on real-time imaging. Ethos enables on-the-fly modifications to treatment plans, ensuring that patients receive more precise and personalized therapy with each fraction. ³⁹ In addition, Elekta has developed AI-enhanced dose planning algorithms, integrating deep learning-based contouring and dose prediction models into its treatment planning software. These AI-powered innovations reduce planning time while improving plan quality and consistency, particularly for intensity-modulated radiation therapy and stereotactic radiosurgery. ⁴⁰

Despite the advancements brought by AI, human oversight remains indispensable in radiation treatment planning. AI-driven automation serves as an adjunct decision-support tool rather than a replacement for clinical expertise. Radiation oncologists play a critical role in ensuring that AI-generated plans align with patient-specific clinical factors, recognizing anatomical variations that AI models may not fully interpret, and integrating their expertise into AI-assisted contouring and dosimetry decisions. A collaborative AI-human approach—where AI automates routine tasks while physicians maintain decision-making authority—is anticipated to become the standard in future treatment planning.

The adoption of AI into treatment planning is moving radiation oncology toward a more automated, precise, and adaptive paradigm. By leveraging machine learning, real-time imaging analysis, and AI-driven dose optimization, clinicians can achieve higher levels of accuracy, efficiency, and patient personalization than ever before.

However, ensuring responsible AI integration, with appropriate regulatory oversight and human involvement, will be crucial to maintaining high standards of patient safety and clinical excellence. As AI continues to evolve and gain acceptance in clinical practice, its role in treatment planning, real-time adaptation, and decision support will likely become indispensable as the field of radiation oncology evolves.

AI in QA, Peer Review, and Safety

Ensuring accuracy, consistency, and safety in radiation therapy is a fundamental priority in oncology. The complexity of modern radiation treatment planning and delivery necessitates robust QA protocols that can catch errors, inconsistencies, and deviations before they impact patient care. Traditionally, manual peer review and QA processes have been integral to radiation oncology, but they are time-intensive, dependent on human interpretation, the relative expertise and attentiveness of the reviewers, and are thus subject to variability. AI-driven automation, predictive analytics, and real-time monitoring are now revolutionizing QA, peer review, and IGRT, significantly enhancing treatment precision and patient safety.

AI-powered QA systems are redefining treatment accuracy and patient safety by introducing automation and standardization into plan verification. These AI models, trained on large datasets of treatment plans, dose distributions, and clinical outcomes, can identify errors, assess plan quality, and highlight deviations from best practices in real-time.

Automated QA tools leveraging machine learning algorithms significantly enhance the evaluation of radiation treatment plans, offering unprecedented speed and consistency. These AI-powered systems are adept at detecting deviations from standard dose constraints, thereby flagging potential errors before treatment delivery. They meticulously analyze beam configurations and dose distributions to identify irregularities potentially causing overdosage or underdosage, ensuring patient safety and treatment efficacy. Moreover, by streamlining physics quality control processes, these tools reduce the time required for manual plan checks, expediting the overall treatment planning workflow. The integration of AI-driven QA systems augments the capabilities of radiation oncology teams, facilitating faster plan approvals while upholding rigorous safety standards. Empirical studies demonstrate that AI-driven QA can match or even surpass human accuracy in detecting dosimetric inconsistencies, providing an additional safeguard against treatment errors. ⁴¹

Peer review has long been a cornerstone of QA in radiation therapy, enabling clinicians to evaluate treatment plans, optimize dose delivery, and ensure adherence to established clinical guidelines. However, the escalating complexity of modern treatment planning, coupled with increasing workload demands, can constrain the depth and thoroughness of manual plan reviews. AI is presently enhancing peer review processes by systematically comparing new treatment plans against a vast repository of historical best practices. AI systems proficiently identify outliers in dose-volume histograms, promoting strict adherence to treatment constraints. In addition, they offer AI-generated recommendations based on extensive datasets comprising thousands of prior cases, providing insights into alternative, potentially superior planning strategies. It is imperative to recognize that AI-assisted peer review serves as an advanced decision-support tool, complementing, but not replacing human judgment. This symbiotic relationship ensures that radiation oncologists can concentrate on higher-level clinical decision-making, while AI manages routine quality checks. ⁴²

The integration of AI into IGRT is markedly enhancing daily treatment accuracy and safety. IGRT employs real-time imaging to verify patient positioning and tumor localization, ensuring that radiation doses are precisely delivered to the intended targets. Several leading institutions have pioneered AI-driven QA and peer review systems. For instance, MSKCC (USA) has integrated deep learning-based QA systems that automate treatment plan evaluations, ensuring adherence to clinical best practices and dosimetric constraints. Likewise, the Netherlands Cancer Institute (Netherlands) has developed AI-assisted peer review platforms that analyze large-scale treatment databases, flagging deviations in dose distribution and plan complexity for further clinician review.

Manual interpretation of IGRT, however, can be susceptible to variability and delays. AI-powered image analysis algorithms address these challenges by detecting anatomical changes in real-time and adjusting treatment plans accordingly. They assess tumor response and regression, facilitating adaptive planning that reflects the dynamic nature of tumor morphology. Furthermore, AI systems predict cumulative radiation dose exposure, enabling proactive management of potential toxicities. These advancements underscore the pivotal role of AI in refining the precision and safety of radiation therapy. By incorporating AI-driven QA and IGRT into daily practice, radiation oncology is transitioning toward a new era of precision and safety, where real-time AI assistance minimizes treatment errors and optimizes therapeutic outcomes.

AI in Treatment Delivery and Adaptive Radiotherapy

AI’s role in radiation oncology extends beyond planning and QA to real-time treatment adaptation and delivery. Traditional radiation therapy has relied on pre-determined treatment plans, which remain fixed throughout the course of therapy. However, tumor shrinkage, anatomical changes, and patient movement can cause the original treatment plan to become suboptimal over time. AI is now transforming radiation delivery into a dynamic, adaptive process, ensuring real-time monitoring and automatic dose adjustments, and enhanced treatment precision. These AI-driven approaches analyze intra-fraction motion, anatomical variations, consideration of resimulation and planning due to tumor and anatomical changes, and real-time imaging data to refine beam delivery and dose adaptation.

The advent of MR-Linacs has significantly advanced the precision of radiation therapy by enabling real-time tumor imaging during treatment. However, real-time manual adjustments are resource intensive. AI-driven motion tracking systems are enhancing the functionality of MR-Linacs by automatically detecting tumor motion patterns, ensuring that radiation beams remain accurately targeted. These systems facilitate real-time beam gating, delivering radiation exclusively when the tumor is correctly positioned, and predict intra-fraction anatomical shifts, thereby reducing the risk of dose misalignment. Deep learning algorithms have demonstrated high accuracy in forecasting motion trajectories, allowing for seamless, real-time adaptation of beam delivery. These AI-driven solutions markedly improve tumor coverage while minimizing unnecessary radiation exposure to surrounding healthy tissues. In addition, AI-driven dose modulation plays a pivotal role in treatment delivery, especially in high-dose SBRT and proton therapy. AI-powered dose modulation systems continuously analyze real-time imaging data, dynamically adjusting dose distributions. They predict anatomical deformations, compensate for unexpected changes in patient positioning, and enhance dose conformity, ensuring optimal tumor coverage while minimizing toxicity. Preliminary studies indicate that AI-enhanced dose adaptation leads to improved local tumor control and reduced treatment-related side effects, representing a significant advancement in personalized radiation therapy. ⁴³

MD Anderson Cancer Center has implemented AI-powered adaptive radiotherapy workflows, integrating motion tracking, auto-segmentation, and real-time adaptive dosing into treatment delivery. This AI-driven approach has significantly improved tumor targeting accuracy, particularly in cases where tumors are prone to motion and anatomical shifts. ⁴⁴ With continued advancements in machine learning, real-time imaging, and adaptive therapy algorithms, AI is expected to play a central role in the next generation of radiation therapy. Future innovations may include fully autonomous AI-driven radiation delivery, where treatment plans are continuously refined in real time; integration of AI with proton therapy and FLASH radiotherapy, optimizing ultra-high-dose-rate radiation for specific cancer types; and expansion of AI-driven adaptive radiotherapy across multiple cancer centers, ensuring that more patients benefit from precision treatment. By embracing AI-driven radiation delivery and adaptive therapy, the field of radiation oncology is entering an era of truly personalized treatment, where each fraction of radiation is optimized to reflect the patient’s real-time anatomical and physiological state. These advancements promise to enhance treatment efficacy, reduce toxicity, and improve overall patient outcomes, ushering in a new standard of precision oncology.

AI in Follow-Up Care, Survivorship, and Long-Term Monitoring

As cancer treatment outcomes continue to improve, long-term survivorship care has become a critical focus in oncology, with the goal of ensuring that survivors maintain optimal health, early detection of recurrence, and effective management of late toxicities. Traditionally, follow-up care relies on scheduled clinical visits and periodic imaging, which may not always detect subtle disease progression or late-onset treatment side effects in a timely manner. AI is now redefining follow-up care by introducing predictive modeling, integration of emerging monitoring techniques such as minimal residual disease detection, remote monitoring, and AI-driven patient engagement tools, offering personalized and proactive survivorship management. More to come on that, for sure.

Radiation therapy, while highly effective in treating cancer, carries the potential for late-onset complications, including secondary malignancies, fibrosis, cardiovascular disease, and neurocognitive effects. AI-powered risk models are now transforming survivorship care by predicting which patients are at higher risk for long-term toxicities based on individualized treatment parameters, genetic predispositions, and real-time patient data.

AI-based risk prediction models are increasingly utilized in radiation oncology to enhance patient care by integrating diverse data sources, including dose distributions, imaging biomarkers, and genetic factors. These models are trained on multi-institutional datasets to predict recurrence patterns, enabling earlier and more tailored interventions. They also identify patients at risk for secondary malignancies, assisting clinicians in personalizing long-term surveillance plans, and assessing long-term organ toxicity risks, thereby improving strategies to minimize radiation-induced complications. For instance, machine learning models have demonstrated 75% accuracy in predicting moderate to severe radiation pneumonitis in lung cancer patients, facilitating early intervention strategies before symptoms arise. ⁴⁵

Another significant advancement in AI-driven survivorship care is the real-time remote monitoring of patients’ symptoms and quality of life. Traditional survivorship models often rely on infrequent, scheduled visits, which may miss early warning signs of complications. AI-enabled remote monitoring tools, integrated with mobile applications and wearable devices, allow clinicians to continuously track patient-reported outcomes (PROs), capturing changes in symptoms, pain levels, and fatigue. These tools can detect early signs of toxicity or recurrence, prompting timely follow-ups when necessary. AI-driven symptom analysis provides real-time, personalized recommendations for supportive care. Studies have shown that such interventions lead to improved patient outcomes, reduced emergency hospital visits, and enhanced quality of life by proactively addressing patient concerns. ⁴⁶

As cancer survivors transition from active treatment to survivorship, many experience uncertainty and anxiety about long-term health maintenance. This is becoming an increasing effort in the oncology workforce, as the NIH estimates there are 18.1 million cancer survivors in the USA, with an estimated 26 million cancer survivors by 2040. AI-driven chatbots and virtual assistants are now playing a vital role in long-term survivorship support by offering personalized health coaching on nutrition, exercise, and mental well-being. They provide medication adherence reminders, ensure compliance with post-treatment medications, and deliver automated answers to survivorship-related questions, thereby reducing the need for unnecessary clinical visits. These AI-driven tools empower survivors to manage their unique health needs more effectively and confidently during the post-treatment phase, effectively leveraging technology to meet the needs of this growing population. One of the leading institutions pioneering AI-driven survivorship care is Stanford Medicine, which has integrated AI-powered platforms for long-term patient engagement and real-time health monitoring. Their research shows that AI-enhanced survivorship programs improve PROs, enhance patient satisfaction, and reduce post-treatment complications. ⁴⁷ AI’s role in follow-up care and survivorship represents a transformational shift toward proactive, personalized, and continuous monitoring, ensuring that cancer survivors receive the highest quality of care long after treatment concludes.

Challenges and Ethical Considerations

The integration of AI into radiation oncology offers transformative potential but also presents several challenges and ethical considerations that must be addressed to ensure safe, equitable, and effective implementation. Key issues include data privacy, algorithmic bias, overreliance by physicians, and the establishment of robust regulatory frameworks.

AI-driven decision support systems enhance clinical workflows; however, they raise concerns regarding transparency, accountability, and the necessity for human oversight. It is imperative that radiation oncologists remain actively engaged in the decision-making process to ensure that AI-generated recommendations are interpretable, clinically valid across diverse patient populations, and aligned with individual patient preferences. This approach mitigates the risk of “black box” decision-making and prevents over-reliance on AI at the expense of clinical expertise. ⁴⁸

The reliance on AI on large-scale datasets for training and optimization introduces significant privacy challenges. Protecting sensitive patient information while facilitating AI-driven advancements necessitates strict data anonymization protocols, the adoption of federated learning approaches that allow AI models to learn from decentralized data without compromising individual privacy, and adherence to international privacy regulations such as the Health Insurance Portability and Accountability Act and the General Data Protection Regulation. Algorithmic bias is another critical concern, as AI models trained on non-representative datasets may perpetuate disparities in treatment recommendations, particularly affecting underrepresented groups. To mitigate this, AI systems should be trained on diverse, multi-institutional datasets that reflect global populations, undergo continuous refinement through prospective clinical trials to ensure robust validation and be regularly audited for bias detection, incorporating fairness metrics into their deployment strategies. ³

Regulatory agencies, including the U.S. FDA and the EMA, are actively developing guidelines for AI-driven medical technologies. These frameworks emphasize the validation and clinical safety of AI models prior to deployment, establish standards for AI explainability to ensure transparency, and mandate ongoing post-market surveillance to monitor the real-world performance of AI applications in clinical settings. ⁴⁹

However, while AI in radiation oncology has demonstrated efficiency gains and improved patient outcomes, there is limited discussion on the economic burden associated with deploying these technologies—particularly in a field already characterized by high costs of technology and infrastructure. AI implementation requires substantial financial investment, including the purchase of advanced AI-driven planning systems, computational hardware, and ongoing software updates. These expenses may be prohibitive for community hospitals and low-resource settings, leading to disparities in access to AI-enhanced treatment planning. In addition, cost-effectiveness analyses are lacking, raising concerns about whether AI truly optimizes resource utilization or merely adds another layer of expense without clear financial benefits.

Beyond financial concerns, infrastructure limitations and workforce training requirements pose further challenges. Effective AI deployment necessitates high-quality imaging systems, robust data storage solutions, and seamless integration with existing treatment planning software. Many community oncology centers may not have the necessary infrastructure to support AI adoption, further widening the gap between flagship institutions and smaller clinical settings. In addition, radiation oncologists, medical physicists, and dosimetrists require specialized training to interpret AI recommendations, troubleshoot errors, and understand model limitations. Without appropriate education, there is a risk of automation bias, where clinicians over-rely on AI-generated outputs without critically assessing their accuracy. Furthermore, “black box” AI models, which lack interpretability, can hinder clinician trust and adoption, as practitioners may be reluctant to use AI-driven recommendations they do not fully understand.

Despite successful implementations of AI-driven treatment workflows in leading academic cancer centers, widespread adoption in community oncology settings faces challenges such as limited access to AI-trained personnel, infrastructure disparities, and variability in regulatory approvals. Addressing these issues requires multidisciplinary collaboration among clinicians, AI researchers, ethicists, and policymakers to ensure that AI serves as a tool to enhance, rather than replace, human expertise in radiation oncology. ⁵⁰ In conclusion, while AI holds significant promise for advancing radiation oncology, careful consideration of economic feasibility, infrastructure needs, workforce training, and ethical concerns is essential to harness its full potential responsibly and equitably.

The Future of AI in Radiation Oncology

As AI continues to evolve, its impact on radiation oncology is poised to extend beyond current applications and into a new era of precision medicine, real-time adaptive therapy, and multi-disciplinary data integration. Future advancements will focus on enhancing automation while preserving the critical role of human expertise, ensuring that AI serves as a collaborative tool rather than a replacement for clinical judgment. The next frontier of AI in radiation oncology will be defined by emerging technologies, global collaboration, and ethical integration into clinical workflows.

The integration of AI with quantum computing is poised to revolutionize real-time ART. Traditional radiation therapy planning often requires extensive computational time to generate optimized dose distributions. Quantum computing, with its capacity to process vast datasets simultaneously, offers the potential for ultra-fast, real-time radiation dose recalculations, enabling instantaneous treatment adaptations. This advancement could enhance AI-driven tumor tracking, ensure precise treatment despite anatomical changes, and optimize complex radiation therapy parameters beyond current computational capabilities. Although still in the early research stages, initial studies suggest that quantum-enhanced AI applications could transform radiation therapy into a dynamic, continuously adapting treatment modality. ⁵¹

Beyond imaging and treatment planning, AI’s role in radiation oncology is expanding to include multi-omics data integration, bridging the gap between radiation therapy and precision medicine. Incorporating genomics, proteomics, and metabolomics data into AI models enables personalized radiation dose adjustments based on a patient’s tumor biology and genetic risk factors, a process currently being utilized in some radiation de-escalation studies. AI-driven analyses can predict radiosensitivity using genomic signatures, ensuring tailored radiation doses, and identifying novel biomarkers to guide combined radiation and immunotherapy approaches, thereby improving treatment efficacy. Institutions such as MD Anderson Cancer Center are leveraging AI for multi-omics-driven radiation therapy, paving the way for biologically adaptive treatments. As AI adoption in radiation oncology accelerates, cross-institutional collaboration becomes essential to ensure that AI models are generalizable, ethical, and widely applicable. The successful integration of AI into clinical practice requires synergistic efforts among academic institutions, governmental agencies, and industry leaders. Public-private partnerships are instrumental in translating AI innovations from research to clinical application, maintaining high safety and ethical standards throughout the process.

The integration of AI with quantum computing and multi-omics data has the potential to profoundly impact clinical practice in radiation oncology, particularly in terms of precision treatment planning and personalized patient care. Quantum computing’s advanced computational capabilities may allow radiation oncologists to analyze complex, multi-layered genomic, proteomic, and radiometric data rapidly and accurately, offering a powerful tool for individualized decision-making. Practically, clinicians will need to consider integrating quantum-powered AI algorithms into existing treatment planning software, necessitating infrastructure upgrades, specialized training, and interdisciplinary collaboration with data scientists. International initiatives, such as the Italian Association of Radiation Oncology (AIRO)’s treatment personalization programs in Italy, the EORTC AI Task Force’s prognostic model development in Europe, and policy and ethics guidance from the American Society of Clinical Oncology (ASCO) and the American Society for Radiation Oncology (ASTRO) in the USA, are establishing frameworks that radiation oncologists can follow to responsibly adopt these cutting-edge technologies. As these efforts continue, radiation oncology departments should prioritize staff education in AI literacy, address workflow modifications to integrate quantum-enhanced AI systems and develop protocols to safeguard patient privacy and data security. This forward-looking approach can ultimately enable radiation oncologists to deliver highly personalized, adaptive radiation treatments that improve clinical outcomes and patient quality of life.

Conclusion

AI is revolutionizing radiation oncology by enhancing precision, optimizing workflow efficiency, and improving accessibility to high-quality cancer care. From automated tumor segmentation to real-time adaptive therapy and long-term survivorship monitoring, AI’s role in the entire radiation oncology continuum is undeniably transformative. However, AI’s true potential is not in replacing clinicians, but rather in empowering them. The fusion of human expertise with AI-driven decision support represents the ideal model for the future of oncology care. While AI can automate repetitive tasks, improve accuracy, and process vast amounts of clinical data, human oversight remains irreplaceable in complex clinical decision-making, ethical judgment, and compassionate patient care. For AI to reach its full potential in radiation oncology, several key actions must be prioritized. First, AI models must undergo robust clinical validation through rigorous testing across diverse patient populations to ensure reliability and generalizability. ⁵² Second, standardized AI training for clinicians is essential; radiation oncologists must acquire AI literacy skills to effectively interpret and integrate AI insights into clinical workflows, enhancing clinical decision-making and patient care.^53,54 Lastly, ethical and regulatory safeguards must be firmly established, ensuring AI adoption aligns with strict patient privacy laws, transparent decision-making standards, and global regulatory frameworks.^55,56

AI will not replace human radiation oncologists. However, a clinician using AI tools will inevitably outperform a clinician who does not. The future of AI in radiation oncology is not about automation replacing expertise, but rather about augmenting human intelligence to achieve levels of precision and personalization never before possible.