The Role of Artificial Intelligence and Machine Learning in Assisted Reproductive Technologies.

Journal: Obstetrics and gynecology clinics of North America
Published Date:

Abstract

Artificial intelligence (AI) and machine learning, the form most commonly used in medicine, offer powerful tools utilizing the strengths of large data sets and intelligent algorithms. These systems can help to revolutionize delivery of treatments, access to medical care, and improvement of outcomes, particularly in the realm of reproductive medicine. Whether that is more robust oocyte and embryo grading or more accurate follicular measurement, AI will be able to aid clinicians, and most importantly patients, in providing the best possible and individualized care. However, despite all of the potential strengths of AI, algorithms are not immune to bias and are vulnerable to the many socioeconomic and demographic biases that current healthcare systems suffer from. Wrong diagnoses as well is furthering of healthcare discrimination are real possibilities if both the capabilities and limitations of AI are not well understood. Armed with appropriate knowledge of how AI can most appropriately operate within medicine, and specifically reproductive medicine, will enable clinicians to both create and utilize machine learning-based innovations for the furthering of reproductive medicine and ultimately achieving the goal of building of healthy families.

Authors

  • Victoria S Jiang
    Division of Reproductive Endocrinology and Infertility, Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Suite 10A, Boston, MA, 02114, USA. vjiang2@mgh.harvard.edu.
  • Zoran J Pavlovic
    Department of Obstetrics and Gynecology/Reproductive Endocrinology and Infertility, University of South Florida, Morsani College of Medicine, 2 Tampa General Circle, 6th Floor, Suite 6022, Tampa, FL 33602, USA.
  • Eduardo Hariton
    Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, Boston, United States.