Literature DB >> 33639520

Making radiotherapy more efficient with FAIR data.

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

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.
Copyright © 2021 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Keywords:  Artificial intelligence; FAIR data; Radiotherapy

Mesh:

Year:  2021        PMID: 33639520     DOI: 10.1016/j.ejmp.2021.01.083

Source DB:  PubMed          Journal:  Phys Med        ISSN: 1120-1797            Impact factor:   2.685


  1 in total

1.  Transparency in quality of radiotherapy for breast cancer in the Netherlands: a national registration of radiotherapy-parameters.

Authors:  Nansi Maliko; Marcel R Stam; Liesbeth J Boersma; Marie-Jeanne T F D Vrancken Peeters; Michel W J M Wouters; Eline KleinJan; Maurice Mulder; Marion Essers; Coen W Hurkmans; Nina Bijker
Journal:  Radiat Oncol       Date:  2022-04-12       Impact factor: 3.481

  1 in total

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