Data solidarity for machine learning for embryo selection: A call for the creation of an open access repository of embryo data

Document Type

Journal Article

Publication Title

Reproductive BioMedicine Online

Volume

45

Issue

1

First Page

10

Last Page

13

PubMed ID

35523713

Publisher

Elsevier

School

School of Medical and Health Sciences

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

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).

DOI

10.1016/j.rbmo.2022.03.015

Access Rights

free_to_read

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