Artificial intelligence in radiation treatment planning: a narrative review from automation to clinical decision support

Background and Objective: Radiation treatment planning is a complex and resource-intensive process that plays a critical role in the quality and safety of radiotherapy. Increasing treatment complexity and growing demands on clinical workflows have motivated the adoption of artificial intelligence (AI) to support planning tasks. While early AI applications focused primarily on automation, recent developments have expanded their role toward clinical decision support. The objective of this review is to provide a comprehensive overview of current AI applications across the treatment planning workflow and to introduce a conceptual framework that characterizes the transition from task-based automation to clinically integrated decision support. Methods: This narrative review summarizes current applications of AI in radiation treatment planning, organized by key stages of the planning workflow. We examine developments in auto-segmentation, dose prediction, automated plan generation, and plan evaluation, with a focus on clinical evidence, translational readiness, and implementation challenges. Key Content and Findings: AI-assisted contouring and knowledge-based planning represent the most mature applications and are increasingly integrated into clinical practice. Emerging AI tools support plan evaluation and optimization by providing benchmarks, exploring trade-offs, and promoting consistency across planners and institutions. Rather than replacing human expertise, these systems function most effectively as decision-support tools that augment clinical judgment. However, challenges related to data quality, generalizability, interpretability, and safety continue to limit widespread adoption. Conclusions: AI is reshaping radiation treatment planning, with a clear shift from task-level automation toward clinically integrated decision support. Responsible translation requires rigorous validation, human-centered design, and attention to equity and safety. When implemented thoughtfully, AI has the potential to enhance decision-making, reduce variability, and improve the quality and consistency of radiotherapy care.