Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2022 Aug 1;34(4):159-163.
doi: 10.1097/GCO.0000000000000796. Epub 2022 Jun 27.

Current trends in artificial intelligence in reproductive endocrinology

Dhananjay Bhaskar  1 , T Arthur Chang  2 , Shunping Wang  3
Affiliations
Review

Current trends in artificial intelligence in reproductive endocrinology

Dhananjay Bhaskar et al. Curr Opin Obstet Gynecol. .

Abstract

Purpose of review: Artificial Intelligence, a tool that integrates computer science and machine learning to mimic human decision-making processes, is transforming the world and changing the way we live. Recently, the healthcare industry has gradually adopted artificial intelligence in many applications and obtained some degree of success. In this review, we summarize the current applications of artificial intelligence in Reproductive Endocrinology, in both laboratory and clinical settings.

Recent findings: Artificial Intelligence has been used to select the embryos with high implantation potential, proper ploidy status, to predict later embryo development, and to increase pregnancy and live birth rates. Some studies also suggested that artificial intelligence can help improve infertility diagnosis and patient management. Recently, it has been demonstrated that artificial intelligence also plays a role in effective laboratory quality control and performance.

Summary: In this review, we discuss various applications of artificial intelligence in different areas of reproductive medicine. We summarize the current findings with their potentials and limitations, and also discuss the future direction for research and clinical applications.

PubMed Disclaimer

References

    1. Mirbabaie M, Stieglitz S, Frick NRJ. Artificial intelligence in disease diagnostics: a critical review and classification on the current state of research guiding future direction. Health Technol 2021; 11:693–731.
    1. Twomey M, Sammon D, Nagle T. The role of information retrieval in the diagnostic/decision making process within the medical appointment: a review of the literature. J Decis Syst 2021; 30:378–409.
    1. Yin J, Ngiam KY, Teo HH. Role of artificial intelligence applications in real-life clinical practice: systematic review. J Med Internet Res 2021; 23:e25759.
    1. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med 2019; 380:1347–1358.
    1. Miotto R, Wang F, Wang S, et al. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform 2018; 19:1236–1246.