Making radiotherapy more efficient with FAIR data

Petros Kalendralis; Matthijs Sloep; Johan van Soest; Andre Dekker; Rianne Fijten

doi:10.1016/j.ejmp.2021.01.083

Making radiotherapy more efficient with FAIR data

Phys Med. 2021 Feb:82:158-162. doi: 10.1016/j.ejmp.2021.01.083. Epub 2021 Feb 24.

Authors

Petros Kalendralis¹, Matthijs Sloep², Johan van Soest², Andre Dekker², Rianne Fijten²

Affiliations

¹ Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands. Electronic address: petros.kalendralis@maastro.nl.
² Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands.

PMID: 33639520
DOI: 10.1016/j.ejmp.2021.01.083

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.

Keywords: Artificial intelligence; FAIR data; Radiotherapy.

MeSH terms

Artificial Intelligence*