Summary: | The last decade has seen an explosion of machine learning (ML) applications in healthcare with mixed and sometimes harmful results, despite much promise and associated hype (Heaven, 2020). A significant reason for these reverses is the premature implementation of machine learning algorithms into clinical practice. In this paper we argue the critical need for “data solidarity” for machine learning for embryo selection. A recent Lancet and Financial Times (FT) 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).
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