Using Artificial Intelligence to Improve the Quality and Safety of Radiation Therapy.
Within artificial intelligence, machine learning (ML) efforts in radiation oncology have augmented the transition from generalized to personalized treatment delivery. Although their impact on quality and safety of radiation therapy has been limited, they are increasingly being used throughout radiation therapy workflows. Various data-driven approaches have been used for outcome prediction, CT simulation, clinical decision support, knowledge-based planning, adaptive radiation therapy, plan validation, machine quality assurance, and process quality assurance; however, there are many challenges that need to be addressed with the creation and usage of ML algorithms as well as the interpretation and dissemination of findings. In this review, the authors present current applications of ML in radiation oncology quality and safety initiatives, discuss challenges faced by the radiation oncology community, and suggest future directions.
Duke Scholars
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Related Subject Headings
- Safety Management
- Radiotherapy Planning, Computer-Assisted
- Radiotherapy Dosage
- Radiotherapy
- Radiation Oncology
- Quality Improvement
- Quality Control
- Nuclear Medicine & Medical Imaging
- Machine Learning
- Humans
Citation
Published In
DOI
EISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- Safety Management
- Radiotherapy Planning, Computer-Assisted
- Radiotherapy Dosage
- Radiotherapy
- Radiation Oncology
- Quality Improvement
- Quality Control
- Nuclear Medicine & Medical Imaging
- Machine Learning
- Humans