Integrating Artificial Intelligence into Radiation Therapy Quality Management: Opportunities and Challenges A Structured Narrative Review with a Proposed Governance Framework (PRISM) for the Saudi Vision 2030 Context

Abstract

Background: Radiation therapy (RT) is among the most technically complex and safety-critical services in modern medicine. Its quality management (QM) programme spans machine performance, treatment planning, patient-specific verification, and delivery. Conventional QM relies heavily on labour-intensive, measurement-based checks that are increasingly strained by rising patient volumes and advanced techniques such as intensity-modulated, stereotactic, and adaptive radiotherapy.
Objective: To synthesise current evidence on the application of artificial intelligence (AI) and machine learning (ML) to RT quality management, to critically examine the associated technical, clinical, ethical, and regulatory challenges, and to propose an implementation framework relevant to the Kingdom of Saudi Arabia.
Methods: A structured narrative review of peer-reviewed literature, professional task group reports (AAPM, ESTRO), and regulatory guidance published between 2009 and 2026, interpreted through Donabedian’s structure–process–outcome model, Reason’s Swiss cheese model, and the risk-based philosophy of AAPM Task Group 100.
Findings: AI demonstrates substantial promise in predictive machine QA, virtual patient-specific QA, automated plan and chart review, contour error detection, real-time delivery monitoring, and natural-language analysis of incident reports. However, adoption is constrained by limited and heterogeneous data, poor generalisability across institutions, model drift, limited explainability, automation bias, the unresolved need to quality-assure the AI tools themselves, and evolving regulatory and liability frameworks.
Conclusion: AI should be positioned as an additional, independent layer of defence rather than a replacement for expert oversight. The proposed PRISM framework (Proactive risk intelligence, Robust validation, Integrated human–AI oversight, Standards-aligned governance, and Monitoring for lifecycle learning) offers a pathway aligned with SFDA, NRRC, SDAIA, SCFHS, and CBAHI requirements and with the Health Sector Transformation Program.