ARTICLE SUMMARY:
In this first part of a series of articles on AI’s role in assisted reproduction, we interview Univfy about its machine-learning tool for IVF prognostication to help guide patient decision-making.
Thanks to the influx of AI, machine learning, machine vision, and other technological advances in data analysis, the infertility industry is experiencing a much-needed overhaul.
As in other clinical and medical device segments, technology can help improve clinical decision-making, automate labor-intensive workflows, and provide consistency to the expertise-dependent tasks with which fertility care is rife.
Innovation is booming in the space. MedTech Strategist counts at least 78 start-ups recently founded to develop solutions across all aspects of the infertility journey—digital health companies are offering home testing and remote support for both the male and female sides of the equation, some are developing devices that improve the accuracy and consistency of procedures, and others have models for expanding clinic capacity so more patients have access to services (see Figure 1). Start-ups have formed to innovate around the payment models themselves, with the rise of fertility-specific insurtech and benefits management for employers (of which Progyny is a great example: having gone public in 2019, it now has a market cap of $1.36 billion. In the same space, Carrott Fertility has raised $114 million to date).
