Revolutionizing Women’s Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology

Summary & key facts

This review looks at how artificial intelligence (AI) is being used in gynecology. It says AI has done well in medicine areas that rely on images, and gynecology uses a lot of imaging too. Although AI use in gynecologic imaging is not as advanced as in some other fields, early studies across areas from urogynecology to oncology show promise. The paper reviews the current state, possible future developments, and the technological and ethical challenges for using AI in clinical care and ensuring accountability.

Key facts:

• Gynecology relies heavily on imaging, which gives visual data about the female reproductive system.
• AI methods, especially machine learning and deep learning, have shown potential applications in gynecology, including urogynecology and oncology.
• The use of AI in gynecologic imaging is currently less noticeable than in some other medical fields, but research interest and studies are increasing.
• The review's goals are to describe the current status of AI in gynecology, outline upcoming developments, and discuss challenges to clinical implementation.
• The paper highlights technological and ethical concerns—such as development, implementation, and accountability—as barriers to adopting AI in gynecologic care.

Abstract

Artificial intelligence has yielded remarkably promising results in several medical fields, namely those with a strong imaging component. Gynecology relies heavily on imaging since it offers useful visual data on the female reproductive system, leading to a deeper understanding of pathophysiological concepts. The applicability of artificial intelligence technologies has not been as noticeable in gynecologic imaging as in other medical fields so far. However, due to growing interest in this area, some studies have been performed with exciting results. From urogynecology to oncology, artificial intelligence algorithms, particularly machine learning and deep learning, have shown huge potential to revolutionize the overall healthcare experience for women's reproductive health. In this review, we aim to establish the current status of AI in gynecology, the upcoming developments in this area, and discuss the challenges facing its clinical implementation, namely the technological and ethical concerns for technology development, implementation, and accountability.

Topics

Artificial Intelligence in Healthcare and Education Endometrial and Cervical Cancer Treatments Radiomics and Machine Learning in Medical Imaging

Categories

Health Informatics Health Sciences Medicine

Tags

Artificial intelligence Biology Computer science Genetics Medical physics Medicine Obstetrics and gynaecology Pregnancy Reproductive medicine Surgery Urinary incontinence Urogynecology