Introduction to Machine Learning in Obstetrics and Gynecology
Sherif A Shazly1, Emanuel C Trabuco, Che G Ngufor
1Department of Obstetrics and Gynecology, the Department of Artificial Intelligence and Informatics, and the Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota.
Abstract:
In the digital age of the 21st century, we have witnessed an explosion in data matched by remarkable progress in the field of computer science and engineering, with the development of powerful and portable artificial intelligence-powered technologies. At the same time, global connectivity powered by mobile technology has led to an increasing number of connected users and connected devices. In just the past 5 years, the convergence of these technologies in obstetrics and gynecology has resulted in the development of innovative artificial intelligence-powered digital health devices that allow easy and accurate patient risk stratification for an array of conditions spanning early pregnancy, labor and delivery, and care of the newborn. Yet, breakthroughs in artificial intelligence and other new and emerging technologies currently have a slow adoption rate in medicine, despite the availability of large data sets that include individual electronic health records spanning years of care, genomics, and the microbiome. As a result, patient interactions with health care remain burdened by antiquated processes that are inefficient and inconvenient. A few health care institutions have recognized these gaps and, with an influx of venture capital investments, are now making in-roads in medical practice with digital products driven by artificial intelligence algorithms. In this article, we trace the history, applications, and ethical challenges of the artificial intelligence that will be at the forefront of digitally transforming obstetrics and gynecology and medical practice in general.
More Related Videos
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
06:19Constructing and Visualizing Models using Mime-based Machine-learning Framework
Published on: July 22, 2025
Related Concept Videos
Introduction to Learning
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
Introduction To Survival Analysis
The primary goal of survival analysis is to estimate survival time—the time...
Overview of Biostatistics in Health Sciences
Introduction to Nonparametric Statistics
One of...
Introduction to Statistics
In statistics, the collection of individuals or objects under study is called population. The idea of sampling is to select a portion of the larger population...
Biostatistics: Overview
Discrete variables are...
