Making radiotherapy more efficient with FAIR data

P. Kalendralis; M. Sloep; J. van Soest; A. Dekker; R. Fijten

doi:10.1016/j.ejmp.2021.01.083

Making radiotherapy more efficient with FAIR data

P. Kalendralis^*, M. Sloep, J. van Soest, A. Dekker, R. Fijten

^*Corresponding author for this work

Research output: Contribution to journal › Editorial › Academic › peer-review

6 Citations (Web of Science)

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.

Original language	English
Pages (from-to)	158-162
Number of pages	5
Journal	Physica Medica: European journal of medical physics
Volume	82
DOIs	https://doi.org/10.1016/j.ejmp.2021.01.083
Publication status	Published - 1 Feb 2021

Keywords

Radiotherapy
FAIR data
Artificial intelligence
RADIOMICS

Access to Document

10.1016/j.ejmp.2021.01.083Licence: CC BY

Cite this

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title = "Making radiotherapy more efficient with FAIR data",

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.",

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AU - Sloep, M.

AU - van Soest, J.

AU - Dekker, A.

AU - Fijten, R.

PY - 2021/2/1

Y1 - 2021/2/1

N2 - 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.

AB - 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.

KW - Radiotherapy

KW - FAIR data

KW - Artificial intelligence

KW - RADIOMICS

U2 - 10.1016/j.ejmp.2021.01.083

DO - 10.1016/j.ejmp.2021.01.083

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