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Background <p>Integrating artificial intelligence (AI) prospected in the practical clinical management of polycystic ovary syndrome (PCOS) promised significant improvement in efficiency, interpretability, and generalizability.</p> Purpose <p>To delineate a comprehensive inventory of AI-driven interventions pertinent to PCOS across diverse clinical contexts.</p> Evidence reviews <p>AI-based analytics profoundly transformed the management of PCOS, particularly in the domains of prediction, diagnosis, classification, and screening of potential complications.</p> Results <p>Our analysis traced the principal applications of AI in PCOS management, focusing on prediction, diagnosis, classification, and screening. Furthermore, this study ventures into the potential of amalgamating and augmenting existing digital health technologies to forge an AI-augmented digital healthcare ecosystem encompassing the prevention and holistic management of PCOS. We also discuss strategic avenues that may facilitate the clinical translation of these innovative systems.</p> Conclusion <p>This systematic review consolidated the latest advancements in AI-driven PCOS management encompassing prediction, diagnosis, classification, and screening of potential complications, developing a digital healthcare framework tailored to the practical clinical management of PCOS.</p>

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Artificial intelligence in polycystic ovarian syndrome management: past, present, and future

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月23日

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  • Jinyuan Wang,
  • Ruxin Chen,
  • Haojun Long,
  • Junhui He,
  • Masong Tang,
  • Mingxuan Su,
  • Renhe Deng,
  • Yuru Chen,
  • Rongqian Ni,
  • Shuhua Zhao,
  • Meng Rao,
  • Huawei Wang,
  • Li Tang

摘要

Background

Integrating artificial intelligence (AI) prospected in the practical clinical management of polycystic ovary syndrome (PCOS) promised significant improvement in efficiency, interpretability, and generalizability.

Purpose

To delineate a comprehensive inventory of AI-driven interventions pertinent to PCOS across diverse clinical contexts.

Evidence reviews

AI-based analytics profoundly transformed the management of PCOS, particularly in the domains of prediction, diagnosis, classification, and screening of potential complications.

Results

Our analysis traced the principal applications of AI in PCOS management, focusing on prediction, diagnosis, classification, and screening. Furthermore, this study ventures into the potential of amalgamating and augmenting existing digital health technologies to forge an AI-augmented digital healthcare ecosystem encompassing the prevention and holistic management of PCOS. We also discuss strategic avenues that may facilitate the clinical translation of these innovative systems.

Conclusion

This systematic review consolidated the latest advancements in AI-driven PCOS management encompassing prediction, diagnosis, classification, and screening of potential complications, developing a digital healthcare framework tailored to the practical clinical management of PCOS.