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<p>Infertility rates are rising globally, leading to increased reliance on assisted reproductive technologies (ART). Despite technological advances, many ART procedures remain highly manual and subjective, with few evidence-based tools available to guide protocols, evaluate and select gametes, or provide the necessary precision in a non-invasive manner. As a result, ART outcomes are often difficult to predict. Artificial intelligence (AI) and automation present promising opportunities to standardize and streamline processes. Some approaches, such as AI-driven embryo analysis and selection, and microfluidic-based semen processing, have already been adopted in clinical settings. However, the full potential of automation, computer vision and deep learning in ART has yet to be realized. This Review examines the development and application of AI and automation across the embryology laboratory, highlighting key innovations, clinical opportunities and ongoing challenges, including ethical and regulatory considerations. We conclude with a systems-level discussion of how these technologies could ultimately converge into an AI-integrated, closed-loop in vitro fertilization laboratory capable of adaptive, data-driven reproductive&#xa0;care.</p>

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AI and automation in assisted reproduction

  • 期刊论文

  • 发表日期：

    2026年6月3日

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  • Jennifer Lorimer,
  • Robert McLachlan,
  • Deirdre Zander-Fox,
  • Moira K. O’Bryan,
  • Reza Nosrati

摘要

Infertility rates are rising globally, leading to increased reliance on assisted reproductive technologies (ART). Despite technological advances, many ART procedures remain highly manual and subjective, with few evidence-based tools available to guide protocols, evaluate and select gametes, or provide the necessary precision in a non-invasive manner. As a result, ART outcomes are often difficult to predict. Artificial intelligence (AI) and automation present promising opportunities to standardize and streamline processes. Some approaches, such as AI-driven embryo analysis and selection, and microfluidic-based semen processing, have already been adopted in clinical settings. However, the full potential of automation, computer vision and deep learning in ART has yet to be realized. This Review examines the development and application of AI and automation across the embryology laboratory, highlighting key innovations, clinical opportunities and ongoing challenges, including ethical and regulatory considerations. We conclude with a systems-level discussion of how these technologies could ultimately converge into an AI-integrated, closed-loop in vitro fertilization laboratory capable of adaptive, data-driven reproductive care.