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探索机器学习在妇科护理中的潜力:综述。

Exploring the potential of machine learning in gynecological care: a review.

机构信息

Harcourt Butler Technical University, Kanpur, India.

出版信息

Arch Gynecol Obstet. 2024 Jun;309(6):2347-2365. doi: 10.1007/s00404-024-07479-1. Epub 2024 Apr 16.

Abstract

Gynecological health remains a critical aspect of women's overall well-being, with profound implications for maternal and reproductive outcomes. This comprehensive review synthesizes the current state of knowledge on four pivotal aspects of gynecological health: preterm birth, breast cancer and cervical cancer and infertility treatment. Machine learning (ML) has emerged as a transformative technology with the potential to revolutionize gynecology and women's healthcare. The subsets of AI, namely, machine learning (ML) and deep learning (DL) methods, have aided in detecting complex patterns from huge datasets and using such patterns in making predictions. This paper investigates how machine learning (ML) algorithms are employed in the field of gynecology to tackle crucial issues pertaining to women's health. This paper also investigates the integration of ultrasound technology with artificial intelligence (AI) during the initial, intermediate, and final stages of pregnancy. Additionally, it delves into the diverse applications of AI throughout each trimester.This review paper provides an overview of machine learning (ML) models, introduces natural language processing (NLP) concepts, including ChatGPT, and discusses the clinical applications of artificial intelligence (AI) in gynecology. Additionally, the paper outlines the challenges in utilizing machine learning within the field of gynecology.

摘要

妇科健康仍然是女性整体健康的一个关键方面,对母婴和生殖结局有深远的影响。本综述综合了妇科健康四个关键方面的最新知识:早产、乳腺癌和宫颈癌以及不孕治疗。机器学习 (ML) 已成为一种变革性技术,有可能彻底改变妇科和妇女保健领域。人工智能的子集,即机器学习 (ML) 和深度学习 (DL) 方法,有助于从大型数据集检测复杂模式,并利用这些模式进行预测。本文探讨了机器学习 (ML) 算法如何在妇科领域用于解决与妇女健康相关的关键问题。本文还研究了在妊娠的初始、中期和晚期阶段将超声技术与人工智能 (AI) 相结合。此外,还探讨了人工智能在每个孕期的不同应用。这篇综述论文概述了机器学习 (ML) 模型,介绍了自然语言处理 (NLP) 概念,包括 ChatGPT,并讨论了人工智能 (AI) 在妇科领域的临床应用。此外,本文还概述了在妇科领域利用机器学习所面临的挑战。

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