Artificial Intelligence for Radiation Treatment Planning: Bridging Gaps From Retrospective Promise to Clinical Reality

• Published in: Clinical oncology (Royal College of Radiologists (Great Britain)) 2025-01, Vol.37, p.103630, Article 103630
• Main Authors: Conroy, L., Winter, J., Khalifa, A., Tsui, G., Berlin, A., Purdie, T.G.
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Machine learning; Medical physics; Radiation oncology; Radiation therapy; Radiation therapy treatment planning
• ISSN: 0936-6555; 1433-2981; 1433-2981
• DOI: 10.1016/j.clon.2024.08.005

Artificial intelligence (AI) radiation therapy (RT) planning holds promise for enhancing the consistency and efficiency of the RT planning process. Despite technical advancements, the widespread integration of AI into RT treatment planning faces challenges. The transition from controlled retrospective environments to real-world clinical settings introduces heightened scrutiny from clinical end users, potentially leading to decreased clinical acceptance. Key considerations for implementing AI RT planning include ensuring the AI model performance aligns with clinical standards, using high-quality training data, and incorporating sufficient data variation through meticulous curation by clinical experts. Beyond technical aspects, factors such as potential biases and the level of trust clinical end users place in AI may present unforeseen obstacles for real-world clinical use. Addressing these challenges requires bridging education and expertise gaps among clinical end users, enabling them to confidently embrace and utilize AI for routine RT planning. By fostering a better understanding of AI capabilities, building trust, and providing comprehensive training, the promises of AI RT planning can be a reality in the clinical setting. This article assesses the current clinical use of AI RT planning and explores challenges and considerations for bridging gaps in knowledge and expertise for AI operationalization, with focus on training data curation, workflow integration, explainability, bias, and domain knowledge. Remaining challenges in clinical implementation of AI RT treatment planning are examined in the context of trust building approaches. •Clinical use of artificial intelligence (AI) radiation therapy (RT) planning lags technical advances.•Clinical end-user trust and bias can impact the success of AI RT planning clinical deployment.•There is a gap between the knowledge required for routine use of AI RT planning and the education of clinical end-user.•Rigorous quality management and model performance monitoring are required for sustained high-quality AI RT planning.