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Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System
Published on: April 11, 2018
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Artificial intelligence in radiation therapy treatment planning: A discrete choice experiment
Milena Lewandowska1, Deborah Street1, Jackie Yim1,2
1Centre for Health Economics Research and Evaluation, University of Technology Sydney, Sydney, New South Wales, Australia.
Journal of Medical Radiation Sciences
|December 20, 2024
Summary
Radiation oncology professionals prioritize artificial intelligence (AI) in treatment planning for time savings and clear reasoning. They also value improved accuracy and cost-effectiveness, while acknowledging AI
Area of Science:
- Oncology
- Medical Physics
- Artificial Intelligence
Background:
- Artificial intelligence (AI) offers potential solutions for healthcare staff shortages and inefficiencies in radiation therapy.
- AI adoption in radiation therapy can standardize protocols, improve quality, enhance patient outcomes, and reduce costs.
- Challenges include employment impacts and algorithmic bias, necessitating careful consideration of trade-offs in AI implementation.
Purpose of the Study:
- To investigate the key characteristics of artificial intelligence (AI) systems that radiation oncology professionals deem most important for adoption in treatment planning.
- To understand professional preferences regarding AI attributes such as accuracy, automation, and impact on workload.
Main Methods:
- An online discrete choice experiment (DCE) was conducted with radiation oncology professionals.
- Participants expressed preferences for AI systems based on five attributes: accuracy, automation, exploratory ability, system compatibility, and workload impact.
- Attitudes towards AI were also surveyed, and choices were analyzed using mixed logit regression.
Main Results:
- Respondents favored AI systems offering significant time savings and providing clear explanations for their reasoning.
- A preference was shown for AI systems that enhance contouring precision over manual methods.
- Professionals emphasized the importance of cost-effectiveness and acknowledged the influence of AI on professional roles and service delivery.
Conclusions:
- This study highlights the priorities of radiation oncology professionals concerning AI in treatment planning.
- Findings offer valuable insights for future economic evaluations and management strategies for AI technologies in radiation therapy.
