Abstract
Given the rapid growth of artificial intelligence (AI) applications in radiotherapy and the related transformations toward the data-driven healthcare domain, this article summarizes the need and usage of the FAIR (Findable, Accessible, Interoperable, Reusable) data principles in radiotherapy. This work introduces the FAIR data concept, presents practical and relevant use cases and the future role of the different parties involved. The goal of this article is to provide guidance and potential applications of FAIR to various radiotherapy stakeholders, focusing on the central role of medical physicists.
Highlights
Given the rapid growth of artificial intelligence (AI) applications in radiotherapy and the related transformations toward the data-driven healthcare domain, this article summarizes the need and usage of the FAIR (Findable, Accessible, Interoperable, Reusable) data principles in radiotherapy
When validating a published radiomics model, researchers discover a variety of label names for the Gross Tumor Volume (GTV), making the correct GTV selection problematic
Aiming to overcome problems like these, we need to implement the FAIR (Findable, Accessible, Interoperable, Reusable) data principles [5]. With this commentary article we would like to present our educational opinion based in our research findings and experience with the implementation of the FAIR data principles, rather than giving an exhaustive view of the FAIR principles. This manuscript is intended to provide an overview of the challenges and opportunities of implementing the FAIR principles in radiotherapy, highlighting the medical physicists’ role
Summary
Given the rapid growth of artificial intelligence (AI) applications in radiotherapy and the related transformations toward the data-driven healthcare domain, this article summarizes the need and usage of the FAIR (Findable, Accessible, Interoperable, Reusable) data principles in radiotherapy. Aiming to overcome problems like these, we need to implement the FAIR (Findable, Accessible, Interoperable, Reusable) data principles [5]. The FAIR principles have the potential to tackle the interoperability and reusability issues, using publicly available ontologies for radiation oncology and radiotherapy [25] such as the ROO [16,17] and other semantic technologies [26,27], such as the Resource Description Framework (RDF) [28].
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