Data solidarity for machine learning for embryo selection: A call for the creation of an open access repository of embryo data
Abstract
The last decade has seen an explosion of machine learning applications in healthcare, with mixed and sometimes harmful results despite much promise and associated hype. A significant reason for the reversal in the reported benefit of these applications is the premature implementation of machine learning algorithms in clinical practice. This paper argues the critical need for ‘data solidarity’ for machine learning for embryo selection. A recent Lancet and Financial Times commission defined data solidarity as ‘an approach to the collection, use, and sharing of health data and data for health that safeguards individual human rights while building a culture of data justice and equity, and ensuring that the value of data is harnessed for public good’ (Kickbusch et al., 2021).
Document Type
Journal Article
Date of Publication
7-1-2022
Volume
45
Issue
1
PubMed ID
35523713
Publication Title
Reproductive BioMedicine Online
Publisher
Elsevier
School
School of Medical and Health Sciences
Copyright
free_to_read
First Page
10
Last Page
13
Comments
Afnan, M., Afnan, M. A. M., Liu, Y., Savulescu, J., Mishra, A., Conitzer, V., & Rudin, C. (2022). Data solidarity for machine learning for embryo selection: a call for the creation of an open access repository of embryo data. Reproductive BioMedicine Online, 45(1), p. 10-13. https://doi.org/10.1016/j.rbmo.2022.03.015