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Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant
Published on: October 6, 2023
Artificial Intelligence for Radiation Treatment Planning: Bridging Gaps From Retrospective Promise to Clinical
L Conroy1, J Winter1, A Khalifa2
1Radiation Medicine Program, Princess Margaret Cancer Centre, 610 University Avenue, Toronto, Ontario, M5G 2M9, Canada; Techna Insitute, University Health Network, 190 Elizabeth St, Toronto, Ontario, M5G 2C4, Canada; Department of Radiation Oncology, University of Toronto, 149 College Street - Stewart Building Suite 504, Toronto, Ontario, M5T 1P5, Canada.
Artificial intelligence (AI) in radiation therapy (RT) planning can improve consistency and efficiency. Overcoming challenges in clinical acceptance requires addressing data quality, bias, and building trust through education and expertise.
Area of Science:
- Medical Physics
- Radiotherapy Oncology
- Artificial Intelligence in Healthcare
Background:
- Artificial intelligence (AI) offers potential for improving radiation therapy (RT) planning consistency and efficiency.
- Widespread clinical integration of AI in RT planning faces significant hurdles despite technical progress.
Purpose of the Study:
- To assess the current clinical use of AI in RT planning.
- To explore challenges and considerations for AI operationalization in RT planning.
- To examine trust-building strategies for AI in clinical settings.
Main Methods:
- Review of current AI RT planning applications.
- Analysis of challenges including data curation, workflow integration, explainability, and bias.
- Exploration of knowledge and expertise gaps for clinical end-users.
Main Results:
- Clinical end-user scrutiny increases in real-world settings, potentially decreasing AI acceptance.
- High-quality, varied training data curated by clinical experts is crucial for AI performance.
- Addressing potential biases and building user trust are key to AI adoption.
Conclusions:
- Bridging education and expertise gaps is essential for clinical end-users to confidently adopt AI for RT planning.
- Fostering understanding of AI capabilities and providing comprehensive training are vital.
- Overcoming implementation challenges requires a focus on trust-building and domain knowledge integration.
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