Role of AI in the Clinical Practice of Radiation Oncology: What Does the Future Hold?

Abstract

Artificial intelligence (AI) is rapidly transforming healthcare and is increasingly entering routine radiation oncology practice. Current applications include image registration, automated segmentation, treatment planning, plan evaluation, image-guided and adaptive radiotherapy, quality assurance, radiomics and prediction of treatment response and toxicity. Among these, deep-learning-based auto-contouring and knowledge-based treatment planning are among the most mature applications. AI is also beginning to bridge radiation oncology with radiology, nuclear medicine, digital pathology, chemotherapy and immunotherapy by integrating multimodal clinical and biological information. However, technical performance does not necessarily translate into clinical benefit. Dataset bias, limited external validation, domain shift, automation bias, lack of explainability, cybersecurity, privacy, regulatory uncertainty and accountability remain important challenges. The future of radiation oncology is therefore unlikely to be one of replacement of clinicians by machines. Rather, AI is likely to become a clinical co-pilot that performs computationally intensive and repetitive tasks while radiation oncologists retain responsibility for interpretation, contextual judgement, communication and ethical decision-making. The challenge for the next generation of radiation oncologists is not to compete with AI but to learn how to work safely and intelligently with it. Ultimately, AI can enhance precision and efficiency, but it cannot replace the human connection at the heart of cancer care.