Grand challenges for medical physics in radiation oncology

Fiorino, C.; Jeraj, Robert; Clark, Catharine H.; Garibaldi, Cristina; Georg, Dietmar; Muren, L.P.; Elmpt, Wouter van; Bortfeld, Thomas; Jornet, N.

doi:10.1016/j.radonc.2020.10.001

Cited by 45 publications

(29 citation statements)

References 70 publications

(87 reference statements)

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“…ESTRO, AAPM, EFOMP) play a role in this field to raise the educational level of medical physicists. ESTRO and AAPM working groups defined ‘adoption of artificial intelligence and automation’ as one of the grand challenges of medical physics in radiation oncology [13] and underlined the need for basic knowledge of AI in the curricula for medical physicists [14] . The EFOMP working group on AI aims to ensure that medical physicists’ professional education, continuous training and competence will follow this significant global development [15] .…”

Section: Discussionmentioning

confidence: 99%

Machine learning applications in radiation oncology: Current use and needs to support clinical implementation

Brouwer

Dinkla

Vandewinckele

et al. 2020

Physics and Imaging in Radiation Oncology

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Background and purpose: The 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 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). 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. Results: In total, 213 medical physicists from 202 radiation oncology centres were included in the analysis. Sixtynine 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. 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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Section: Discussionmentioning

confidence: 99%

Machine learning applications in radiation oncology: Current use and needs to support clinical implementation

Brouwer

Dinkla

Vandewinckele

et al. 2020

Physics and Imaging in Radiation Oncology

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“…Further, image reconstruction approaches and radiation treatment plan optimization algorithms also benefit from AI-driven solutions to allow for fast and accurate results [73][74][75]. Finally, the complexities of integrating the various hardware and software components of standard and advanced RT workflows can be addressed only through a multi-disciplinary team effort that supports active interactions between medical physicists, medical doctors and technologists, along with engineers, IT and datascience engineers [76][77][78][79].…”

Section: Rt Physicsmentioning

confidence: 99%

Medical Physics and Imaging–A Timely Perspective

et al. 2021

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“…In the AI framework, Medical Physicists have the knowledge needed to implement tests and use imaging systems based on AI [1,24,66,68]. They are experts in all imaging modalities and know their strengths and weaknesses.…”

Section: The Role Of Medical Physicistsmentioning

confidence: 99%

“…As reported in Fiorino et al [68] Medical Physicists are not new to implementing and customizing new systems in a Hospital, usually collaborating with other specialists. In this framework, Medical Physicists must collaborate with all healthcare specialists to successfully introduce AI-based solutions in the clinical workflows.…”

Section: The Role Of Medical Physicistsmentioning

confidence: 99%