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<p>Artificial Intelligence (AI) is revolutionizing reproductive medicine by enhancing fertility treatments, childbirth monitoring, and postnatal care. AI-driven technologies, including machine learning (ML), natural language processing (NLP), and robotic-assisted surgery, improve diagnostic accuracy, optimize treatment plans, and support clinical decision-making in assisted reproductive technology (ART). While AI has demonstrated significant potential, challenges such as ethical concerns, data privacy risks, algorithmic biases, and regulatory uncertainties remain critical barriers to widespread adoption in reproductive healthcare. This chapter explores the role, advancements, and challenges of AI in reproductive medicine, highlighting its impact on fertility, pregnancy, and postnatal care. A comprehensive analysis of AI applications in reproductive healthcare, including embryo selection, sperm and oocyte quality assessment, ovulation tracking, prenatal diagnostics, labor monitoring, and postpartum depression detection. AI aids in early detection of obstetric complications, optimizes neonatal care in intensive care units (NICUs), and enhances fertility treatments through predictive analytics. AI-driven virtual assistants support maternal health, while machine learning-based tools improve diagnostic precision and clinical decision-making. Addressing ethical and regulatory concerns is essential for equitable AI integration in reproductive medicine. Future advancements require interdisciplinary collaboration, standardized validation through randomized controlled trials, and responsible AI deployment to enhance patient outcomes while maintaining clinical expertise and ethical integrity.</p>

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AI in Childbirth and Emerging Alternatives to Traditional Fertility Treatments

  • 期刊论文

  • 发表日期：

    2026年3月2日

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  • Vandana Bhatia,
  • Anjali Chandel,
  • Yavnika Minhas,
  • Aditya Rattan,
  • Swati Rana

摘要

Artificial Intelligence (AI) is revolutionizing reproductive medicine by enhancing fertility treatments, childbirth monitoring, and postnatal care. AI-driven technologies, including machine learning (ML), natural language processing (NLP), and robotic-assisted surgery, improve diagnostic accuracy, optimize treatment plans, and support clinical decision-making in assisted reproductive technology (ART). While AI has demonstrated significant potential, challenges such as ethical concerns, data privacy risks, algorithmic biases, and regulatory uncertainties remain critical barriers to widespread adoption in reproductive healthcare. This chapter explores the role, advancements, and challenges of AI in reproductive medicine, highlighting its impact on fertility, pregnancy, and postnatal care. A comprehensive analysis of AI applications in reproductive healthcare, including embryo selection, sperm and oocyte quality assessment, ovulation tracking, prenatal diagnostics, labor monitoring, and postpartum depression detection. AI aids in early detection of obstetric complications, optimizes neonatal care in intensive care units (NICUs), and enhances fertility treatments through predictive analytics. AI-driven virtual assistants support maternal health, while machine learning-based tools improve diagnostic precision and clinical decision-making. Addressing ethical and regulatory concerns is essential for equitable AI integration in reproductive medicine. Future advancements require interdisciplinary collaboration, standardized validation through randomized controlled trials, and responsible AI deployment to enhance patient outcomes while maintaining clinical expertise and ethical integrity.