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

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).

Automated summary

The past decade has witnessed a rapid growth of machine learning applications in healthcare, but the premature implementation of these algorithms has led to mixed and sometimes negative outcomes. This paper emphasizes the critical need for "data solidarity" in machine learning for embryo selection, which involves ensuring individual rights, promoting data justice and equity, and utilizing data for the public good.