Tadepalli Kalyan, Das Abhijit, Meena Tanushree, Roy Sudipta
Sir HN Reliance Foundation Hospital, Girgaon, Mumbai, 400004, India; Artificial Intelligence & Data Science, Jio Institute, Navi Mumbai, 410206, India.
Artificial Intelligence & Data Science, Jio Institute, Navi Mumbai, 410206, India.
Comput Methods Programs Biomed. 2025 May;263:108682. doi: 10.1016/j.cmpb.2025.108682. Epub 2025 Feb 23.
This review aims to comprehensively explore the application of Artificial Intelligence (AI) to an area that has not been traditionally explored in depth: the continuum of maternal-fetal health. In doing so, the intent was to examine this physiologically continuous spectrum of mother and child health, as well as to highlight potential pitfalls, and suggest solutions for the same.
A systematic search identified studies employing AI techniques for prediction, diagnosis, and decision support employing various modalities like imaging, electrophysiological signals and electronic health records in the domain of obstetrics and fetal health. In the selected articles then, AI applications in fetal morphology, gestational age assessment, congenital defect detection, fetal monitoring, placental analysis, and maternal physiological monitoring were critically examined both from the perspective of the domain and artificial intelligence.
AI-driven solutions demonstrate promising capabilities in medical diagnostics and risk prediction, offering automation, improved accuracy, and the potential for personalized medicine. However, challenges regarding data availability, algorithmic transparency, and ethical considerations must be overcome to ensure responsible and effective clinical implementation. These challenges must be urgently addressed to ensure a domain as critical to public health as obstetrics and fetal health, is able to fully benefit from the gigantic strides made in the field of artificial intelligence.
Open access to relevant datasets is crucial for equitable progress in this critical public health domain. Integrating responsible and explainable AI, while addressing ethical considerations, is essential to maximize the public health benefits of AI-driven solutions in maternal-fetal care.
本综述旨在全面探索人工智能（AI）在一个传统上未被深入探索的领域的应用：母婴健康连续体。这样做的目的是审视母婴健康这一生理上连续的频谱，突出潜在的陷阱，并提出相应的解决方案。
通过系统检索，确定了在产科和胎儿健康领域采用人工智能技术进行预测、诊断和决策支持的研究，这些研究采用了多种模式，如图像、电生理信号和电子健康记录。在所选定的文章中，从该领域和人工智能的角度对人工智能在胎儿形态学、孕周评估、先天性缺陷检测、胎儿监测、胎盘分析和母体生理监测中的应用进行了批判性审视。
人工智能驱动的解决方案在医学诊断和风险预测方面展现出了有前景的能力，提供了自动化、更高的准确性以及个性化医疗的潜力。然而，必须克服数据可用性、算法透明度和伦理考量等挑战，以确保负责任且有效的临床应用。必须紧急应对这些挑战，以确保像产科和胎儿健康这样对公众健康至关重要的领域能够充分受益于人工智能领域取得的巨大进展。
开放获取相关数据集对于这一关键公共卫生领域的公平进展至关重要。在考虑伦理因素的同时，整合负责任且可解释的人工智能对于最大化人工智能驱动的解决方案在母婴护理中的公共卫生效益至关重要。
