Ethics of artificial intelligence in embryo assessment: mapping the terrain

Artificial intelligence (AI) has the potential to standardize and automate important aspects of fertility treatment, improving clinical outcomes. One promising application of AI in the fertility clinic is the use of machine learning (ML) tools to assess embryos for transfer. The successful clinical implementation of these tools in ways that do not erode consumer trust requires an awareness of the ethical issues that these technologies raise, and the development of strategies to manage any ethical concerns. However, to date, there has been little published literature on the ethics of using ML in embryo assessment. This mini-review contributes to this nascent area of discussion by surveying the key ethical concerns raised by ML technologies in healthcare and medicine more generally, and identifying which are germane to the use of ML in the assessment of embryos. We report concerns about the 'dehumanization' of human reproduction, algorithmic bias, responsibility, transparency and explainability, deskilling, and justice.

Deep technology for the optimization of cryostorage

Cryopreservation, for many reasons, has assumed a central role in IVF treatment cycles, which has resulted in rapidly expanding cryopreserved oocyte and embryo inventory of IVF clinics. We aspire to consider how and with what resources and tools "deep" technology can offer solutions to these cryobiology programs. "Deep tech" has been applied as a global term to encompass the most advanced application of big data analysis for the most informed construction of algorithms and most sophisticated instrument design, utilizing, when appropriate and possible, models of automation and robotics to realize all opportunities for highest efficacy, efficiency, and consistency in a process.