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<p>Primary ovarian insufficiency (POI) is also known as premature ovarian failure (POF), defined as loss of normal, predictable ovarian activity before the age of 40&#xa0;years. In addition, POI patients suffer from long-term complications such as osteoporosis, cardiovascular disease, and depression. The impact on women caused by POI and its complications make it become a major health challenge worldwide. The recent advances in digital health technology (DHT), especially artificial intelligence (AI), provide an important opportunity to improve the efficiency of the management of POI. Clinical work has improved the efficiency of healthcare with the assistance of AI, enabling clinicians to improve clinical treatment efficiency, and mitigate the differences in healthcare level caused by suboptimal resource allocation. This article reviews the application progress of AI in the treatment of POI in recent years, and discusses the opportunities and challenges of AI in clinical application. In addition, we explored the integration of existing digital health technology resources to discuss an AI-assisted eco-smart healthcare system for the treatment of POI.</p>

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Artificial intelligence in primary ovarian insufficiency management: opportunities and challenges

  • 期刊论文

  • 发表日期：

    2025年9月23日

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  • RunTang Zhou,
  • YanHong Wei,
  • Yingguan Xiong,
  • BingBing Su,
  • JunHao Xie,
  • Linlin Hu,
  • XiaoCan Lei

摘要

Primary ovarian insufficiency (POI) is also known as premature ovarian failure (POF), defined as loss of normal, predictable ovarian activity before the age of 40 years. In addition, POI patients suffer from long-term complications such as osteoporosis, cardiovascular disease, and depression. The impact on women caused by POI and its complications make it become a major health challenge worldwide. The recent advances in digital health technology (DHT), especially artificial intelligence (AI), provide an important opportunity to improve the efficiency of the management of POI. Clinical work has improved the efficiency of healthcare with the assistance of AI, enabling clinicians to improve clinical treatment efficiency, and mitigate the differences in healthcare level caused by suboptimal resource allocation. This article reviews the application progress of AI in the treatment of POI in recent years, and discusses the opportunities and challenges of AI in clinical application. In addition, we explored the integration of existing digital health technology resources to discuss an AI-assisted eco-smart healthcare system for the treatment of POI.