The Rise of Artificial Intelligence in Oncology: Opportunities, Challenges, and Future Directions

Abstract

Artificial intelligence (AI) is rapidly transforming oncology by supporting clinical decision-making across the entire cancer care continuum, with radiation oncology emerging as one of the leading specialities in its clinical adoption due to its inherently digital workflow. This narrative review summarises the current evidence on AI applications in oncology, with particular emphasis on radiation oncology, highlighting recent advances in machine learning, deep learning, large language models (LLM), and multimodal AI, while also discussing the challenges and future directions for clinical implementation. AI has demonstrated significant potential in cancer screening, imaging, pathology, genomics, treatment selection, and outcome prediction. Within radiation oncology, it enhances auto- segmentation, treatment planning, image guidance, adaptive radiotherapy, radiomics, and quality assurance (QA), improving workflow efficiency, consistency, and treatment precision. Emerging technologies such as multimodal AI, LLM, federated learning, and digital twins are expected to further accelerate the development of precision oncology. However, widespread clinical implementation continues to face challenges, including limited external validation, algorithmic bias, poor model interpretability, and ethical and regulatory concerns. Overall, AI is becoming an integral component of modern oncology, particularly radiation oncology, and its successful integration into routine clinical practice will require robust validation, transparent governance, equitable implementation, and continued clinician oversight to ensure safe, effective, and patient-centred cancer care.