The DIY IVF cycle—harnessing the power of deeptech to bring ART to the masses

Brayboy, Lynae M.; Quaas, Alexander

doi:10.1007/s10815-022-02691-x

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…Second, efficient selection should reduce the number of treatment cycles needed for a successful pregnancy, lowering the overall costs. Third, costs would be lower with some routine treatment cycle monitoring done at home through self-tracking, for example, using follicle ultrasound devices with AI algorithmic software, making it unnecessary to travel to healthcare providers for every step in the process (Brayboy and Quaas, 2022; Ho, 2023; Rolfes et al, 2023; Tamir, 2023), and thereby generating geographical equality in access (Ho, 2023).…”

Section: Access To Ai Technology In Assisted Reproductionmentioning

confidence: 99%

Artificial intelligence and assisted reproductive technology: Applying a reproductive justice lens

Homanen,

McBride,

Hudson

2024

European Journal of Women's Studies

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In recent years, health-data-driven artificial intelligence and machine learning applications have been introduced to many areas of medicine. In the field of assisted reproduction, artificial intelligence and machine learning applications and related technologies have been hailed as (potentially) significant and ground-breaking, not least because they promise standardisation and automation in in-vitro fertilisation clinics – a precondition for scaling up and branching out in the fertility bioindustry. Artificial intelligence data-driven algorithms promise time- and cost-effective selection of ‘high-quality’ reproductive cells and successful personalised treatments. In this essay, we aim to critically discuss artificial intelligence as a technological clinical practice, which is currently moving from bench to bedside internationally. Through an analytic framework of reproductive justice, we propose that introducing artificial intelligence into this already stratified context threatens to black-box health disparities and to generate what we refer to as ‘hyper-stratifications’ of reproduction in the context of rising health and social disparities in the European context. As feminist, social science and bioethics scholars, we are all too aware of how reproductive technologies reinforce normativities rather than unravel them. We cannot presume that artificial intelligence is an ethical technological agent or user of health data but, instead, need to keep a critical eye on the moral ambivalence of emerging and evolving artificial intelligence-assisted reproduction technologies practices and their gendered consequences. Given the current hype around artificial intelligence, but also with concerns around the fast development and deployment of artificial intelligence generally and in artificial intelligence-assisted reproduction technologies particularly in mind, there is an urgent need to engage in critical feminist discussion of such developments.

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