Making radiotherapy more efficient with FAIR data. (February 2021)

• Record Type: Journal Article
• Authors: Kalendralis, Petros; Sloep, Matthijs; van Soest, Johan; Dekker, Andre; Fijten, Rianne
• Is Part Of: Physica medica. Volume 82(2021)
• Page Start: 158
• Page End: 162
• Publication Date: 2021-02
• Subjects: Radiotherapy -- FAIR data -- Artificial intelligence; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2021.01.083
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: Introduction of the FAIR data principles in radiotherapy. Artificial Intelligence applications begin to manifest themselves in radiotherapy. Examples of FAIR data in radiotherapy. Stakeholders action items for the implementation of FAIR principles. 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.