Artificial intelligence in radiotherapy: Applications and clinical integration challenges in organ and lesion segmentation, treatment planning, and quality assurance

Radiotherapy (RT) represents a core treatment modality in oncology. Its workflow encompasses multiple critical stages: patient immobilization and positioning, imaging simulation, target contouring, treatment planning, quality assurance (QA), and treatment delivery in sequence. Despite technological advances, many steps remain heavily dependent on manual operations, leading to human-induced uncertainties and limited efficiency and standardization. In this context, artificial intelligence (AI) has emerged as a transformative force in radiation oncology, providing innovative solutions to automate repetitive tasks, enhance clinical decision-making, and personalize treatment strategies. This study presents a comprehensive review of AI applications across three core domains of the RT workflow: (1) automatic segmentation of organs at risk (OARs) and tumor targets, (2) AI-driven treatment planning, including dose prediction and optimization, and (3) AI-assisted quality assurance, encompassing patient-specific, machine-specific, and outcome-based evaluations. For each domain, it presents a summary of representative algorithmic strategies, evaluates their clinical performance, and discusses current limitations. Beyond task-specific automation, this study examines the clinical integration of AI tools throughout the entire RT workflow, including imaging-oriented AI, conversational AI, and large language model (LLM) applications. Furthermore, this study critically analyzes the technical, clinical, and regulatory challenges that impede widespread AI adoption, including limited model generalizability, poor interpretability, data heterogeneity, automation bias, and a lack of standardized validation frameworks. In addition, this study explores the evolving roles of human-AI collaboration, safety considerations, and responsible deployment strategies in clinical practice. Finally, this study outlines future directions for AI in RT, emphasizing the need for multicenter validation, workflow-level evaluations, and interdisciplinary collaboration to translate AI's technical promise into tangible improvements in patient care. The ultimate goal of AI application in RT is to augment the cognitive capabilities of clinicians rather than replace them, thus enabling more precise, efficient, and equitable cancer treatment worldwide.