Fox News contributor Dr. Marc Siegel weighs in on how artificial intelligence can change the patient-doctor relationship on'America's Newsroom.' It's been shown to detect cancer, pinpoint cavities and answer medical questions -- and now, artificial intelligence may help fertility doctors select the ideal embryo for in-vitro fertilization (IVF). With one in every five U.S. adult married women unable to get pregnant after a year of trying, per the Centers for Disease Control and Prevention (CDC), many turn to IVF for help. The fertility treatment is responsible for between 1% and 2% of all births in the country. The process, however, is not guaranteed -- and it's expensive, averaging more than $12,000 per session, according to the American Society of Reproductive Medicine.

People in the U.S. are waiting longer to have babies. And more and more families are seeking help with getting pregnant. In fact, according to Penn Medicine, one million babies have been born between 1987 and 2015 using in vitro fertilization or other assisted technology. But IVF success rates remain relatively low. There is a 21.3% chance of full-term normal birth weight and singleton live birth per assisted reproductive technology cycle, Penn Medicine states.

Louise Brown, the world's first "test tube" baby, was born in 1978 through in vitro fertilization (IVF). In IVF, sperm and egg meet outside the body and the best resulting embryo is implanted into the womb in hopes of a healthy pregnancy. "There have been so many advancements in the bioscience aspects of IVF since then, but it lags behind other healthcare verticals big time as far as data science -- and there is a lot of data that is not being used," Eran Eshed, CEO and cofounder of Fairtility, tells ISRAEL21c. "Today, the process of assisted reproductive technology is based on experience and subjective judgment. The outcomes are not very impressive – it takes an average of three IVF cycles per patient to take a baby home," says Eshed.