Abstract
Background and purposeThe use of artificial intelligence (AI)/ machine learning (ML) applications in radiation oncology is emerging, however no clear guidelines on commissioning of ML-based applications exist. The purpose of this study was therefore to investigate the current use and needs to support implementation of ML-based applications in routine clinical practice.Materials and methodsA survey was conducted among medical physicists in radiation oncology, consisting of four parts: clinical applications (1), model training, acceptance and commissioning (2), quality assurance (QA) in clinical practice and General Data Protection Regulation (GDPR) (3), and need for education and guidelines (4). Survey answers of medical physicists of the same radiation oncology centre were treated as a separate unique responder in case reporting on different AI applications.ResultsIn total, 213 medical physicists from 202 radiation oncology centres were included in the analysis. Sixty-nine percent (1 4 7) was using (37%) or preparing (32%) to use ML in clinic, mostly for contouring and treatment planning. In 86%, human observers were still involved in daily clinical use for quality check of the output of the ML algorithm. Knowledge on ethics, legislation and data sharing was limited and scattered among responders. Besides the need for (implementation) guidelines, training of medical physicists and larger databases containing multicentre data was found to be the top priority to accommodate the further introduction of ML in clinical practice.ConclusionThe results of this survey indicated the need for education and guidelines on the implementation and quality assurance of ML-based applications to benefit clinical introduction.
Highlights
Machine learning (ML) applications are evolving from the academic research departments and turning into commercially available products
In this paper we aim to reveal the current needs for medical physi cists and present the gap in knowledge that needs to be bridged for successful introduction of machine learning (ML)-based applications in clinical practice
A survey was designed to determine to what extent ML-based ap plications are currently implemented in clinical practice, how medical physicists see their role in the near future and what is needed for safe and efficient clinical introduction
Summary
Machine learning (ML) applications are evolving from the academic research departments and turning into commercially available products. Mathematical education of AI techniques is not in the standard curriculum of medical physicists and some medical physicists may feel uncomfortable with this new technology [6] Another difference is that for some ML models such as those used for automatic segmentation or treatment planning are usually trained on large (clinical) datasets. Materials and methods: A survey was conducted among medical physicists in radiation oncology, consisting of four parts: clinical applications (1), model training, acceptance and commissioning (2), quality assurance (QA) in clinical practice and General Data Protection Regulation (GDPR) (3), and need for education and guidelines (4). Conclusion: The results of this survey indicated the need for education and guidelines on the implementation and quality assurance of ML-based applications to benefit clinical introduction
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