AI and machine learning are continually proving to be major assets in the fight to carve out new as well as improve existing pregnancy risk assessment solutions. One of the bright spots in the maternity healthcare space of late has been the employment of AI-based models to make labor and delivery safer for mothers and their newborns. The advantages leveraged through AI in this cause are sorely needed, because despite the U.S. seeing a notable downturn in overall birth rates, labor and delivery complications are on the rise.
The Blue Cross Blue Shield Association tracked this concerning trend from 2014 to 2018, with research finding the chance of delivery issues arising in the average patient had surged by over 14% in only four years. Beyond the long-term, serious health problems are possible for new mothers given this drastic increase, and funding their treatments comes at a high price. Commonwealth Fund research found that dealing with the social costs of pregnancy and childbirth peril could cost $32.4 billion annually in the near future.
The opportunities opened up with AI technology, prized for its convenience and affordability in healthcare solution integration, are multifarious. Mayo Clinic researchers have detailed the emergence of a new AI-powered diagnostic model that stays apprised of a woman’s time in labor, up to the split second. Its algorithm can predict delivery outcomes, even calculating an “unfavorable labor outcome” if the risk is high enough for the mother and/or baby.