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<p>Infertility represents a growing global health challenge, intensifying the demand for advanced assisted reproductive technology (ART). Artificial intelligence (AI) is emerging as a transformative force in reproductive medicine, offering novel solutions to augment clinical success and optimize patient-centered care. This review comprehensively synthesizes AI advancements across the continuum of ART, including sperm and oocyte evaluation, embryo selection, pregnancy prediction, fertility assessment, and supportive nursing. Through the integration of multimodal data, extraction of discriminative features, and construction of predictive models, AI introduces unprecedented objectivity and precision into gamete and embryo analysis, thereby facilitating personalized treatment strategies. Furthermore, intelligent consultation and management systems powered by large language models are redefining reproductive healthcare delivery by enhancing clinician-patient communication and improving engagement. While challenges pertaining to data privacy and model generalizability remain, the deep integration of AI with reproductive medicine is an irreversible trend poised to overcome existing ART bottlenecks and forge a more efficient, humane diagnostic and therapeutic ecosystem.</p>

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AI-enabled precision reproductive medicine: a comprehensive review of clinical applications, decision frameworks, and evidence-based implementation

  • 期刊论文

  • 发表日期：

    2026年6月24日

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  • Xinmiao Hu,
  • Zhiying Chen,
  • Hui Luo

摘要

Infertility represents a growing global health challenge, intensifying the demand for advanced assisted reproductive technology (ART). Artificial intelligence (AI) is emerging as a transformative force in reproductive medicine, offering novel solutions to augment clinical success and optimize patient-centered care. This review comprehensively synthesizes AI advancements across the continuum of ART, including sperm and oocyte evaluation, embryo selection, pregnancy prediction, fertility assessment, and supportive nursing. Through the integration of multimodal data, extraction of discriminative features, and construction of predictive models, AI introduces unprecedented objectivity and precision into gamete and embryo analysis, thereby facilitating personalized treatment strategies. Furthermore, intelligent consultation and management systems powered by large language models are redefining reproductive healthcare delivery by enhancing clinician-patient communication and improving engagement. While challenges pertaining to data privacy and model generalizability remain, the deep integration of AI with reproductive medicine is an irreversible trend poised to overcome existing ART bottlenecks and forge a more efficient, humane diagnostic and therapeutic ecosystem.