Adapting training for medical physicists to match future trends in radiation oncology

Clark, Catharine H.; Gagliardi, Giovanna; Heijmen, B.; Malicki, Julian; Thorwarth, Daniela; Verellen, Dirk; Muren, L.P.

doi:10.1016/j.phro.2019.09.003

Cited by 6 publications

(7 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…Medical physicists dedicated to clinical activities need training for these new developments and programs for commissioning and quality assurance need to be developed. Basic knowledge of AI in the curricula for medical physicists must be secured [46]. The extent of training needed will depend on the specific implementations but as AI applications will be classified as medical devices, the medical physicists should also approach these techniques as such with proper testing and validation (QA) programs in place [47].…”

Section: Development and Implementation Of Artificial Intelligence In Radiation Oncologymentioning

confidence: 99%

“…Modern teaching curricula should include more materials on clinical trial design and more emphasis should be put on adding training sessions in clinical trial design specifically aimed at medical physicists at national and international meetings. 4) In order to maximize the contribution of medical physicists, their education needs to substantially corroborate biology and radiobiological skills to a larger extent than currently captured in our curricula [2,4,6,46]. In addition, medical physicists should acquire a sufficient level of data analytics expertise (e.g., biostatistics, big data, AI) that can constructively contribute to clinical trial design, execution and analyses.…”

Section: Modeling Biological Effects In the Era Of Personalized Radiation Oncologymentioning

confidence: 99%

“…This may cause frustration in medical physics teams, therefore it is crucial to have the right leaders in the right position with the right education and training and networks to drive innovation. Not secondarily, educating outsiders (policy makers, mangers) and the public regarding the essential role of medical physicists is also relevant [46,68]. 4) The fact that radiation oncology has worked so well in the past is because medical physicists were part of the team.…”

Section: Leadership In the Changing World Of Radiation Oncologymentioning

confidence: 99%

“…It is likely that the new challenges will rapidly translate into new roles and responsibilities that should gradually integrate and extend the current legal framework for the MPE in radiation oncology. New requests mean new (multi-disciplinary) fields to be explored, and this means the need for continuously updating skills and education, adapting our core curricula [46]. This point is likely to be one of the most challenging, due to the heterogeneous situations in the different European countries and to the intrinsically slow reactivity of the education systems.…”

Section: Leadership In the Changing World Of Radiation Oncologymentioning

confidence: 99%

See 2 more Smart Citations