Walter G. Johnson is a research project coordinator at the Sandra Day O’Connor College of Law, where he received his J.D. in 2020. He also holds a Master of Science and Technology Policy (2017) and B.S. in Chemistry (2015) from Arizona State University. Walter’s research covers regulatory policy and governance for various emerging technologies and his writing has appeared in forums such as the Food & Drug Law Journal, the Washington Post, and the Journal of Law & the Biosciences. Read more about his research on AI and Fertility Clinics here: 60 Jurimetrics J. 247 (2020).
A lot has changed in the four decades since the first birth from in vitro fertilization (IVF).
Many fertility clinics have opened, new methods have emerged, and well over 8 million people have been born through IVF or other assisted reproductive technologies (ARTs). In the last several years, even more changes have become possible – including the case of three infants born in China in 2018 and 2019 after their genes were edited with CRISPR tools. Artificial intelligence (AI) is also starting to play a bigger role in fertility clinics, which Dean Diana Bowman and I realized while researching these trends for her work funded by the Carnegie Foundation.
Data from the CDC suggests that every time a couple tries an ART treatment, the chances of it leading to a birth are only about 25% – though, of course, this depends on many factors. Because doctors often only transfer one or two embryos to a patient at a time (for safety reasons), using AI to predict which embryos are most likely to start a pregnancy could save time, money, and heartbreak for families. Companies like Vitrolife and Life Whisperer are already working on these types of AI applications, which watch embryos grow for a few days in the lab before making suggestions to doctors.
In our recent SciTech Corner in Jurimetrics, we explore this new use of AI in fertility
clinics, talk about its potential benefits, and raise a few concerns. For example, AI don’t understand what a cell is or why embryos look the way they do – the AI is just looking for patterns in pictures. So, how will we know that the program is using the right information to make predictions and suggestions? These are hard questions, and regulators and insurance providers will have to be satisfied that the AI is good enough before it can take off in fertility clinics. We argue that doctors have a role to play too, in learning from their own experience and helping patients make informed choices about these upcoming ART techniques.
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