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

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

doi:10.1007/s11547-025-02032-9

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

Radiol Med. 2025 Jun 23. doi: 10.1007/s11547-025-02032-9. Online ahead of print.

Authors

Jinyuan Wang^#¹, Ruxin Chen^#², Haojun Long³, Junhui He⁴, Masong Tang⁵, Mingxuan Su⁶, Renhe Deng⁷, Yuru Chen¹, Rongqian Ni¹, Shuhua Zhao¹, Meng Rao¹, Huawei Wang⁸, Li Tang⁹

Affiliations

¹ Department of Reproduction and Genetics, The First Affiliated Hospital of Kunming Medical University, 295 Xichang Road, Kunming, 650032, Yunnan Province, China.
² Department of Gynecological Endocrinology, Jinan Maternity and Child Care Hospital Affiliated to Shandong First Medical University, Jinan, 250001, China.
³ Department of Dermatology, The Second Affiliated Hospital of Kunming Medical University, Kunming, 650101, Yunnan, China.
⁴ Key Laboratory for Experimental Teratology of the Ministry of Education and Center for Experimental Nuclear Medicine, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.
⁵ Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
⁶ Clinical Anatomy & Reproduetive Medieine Application Institute, Hengyang Medieal Sehool, University of South China, Hengyang, China.
⁷ Department of Plastic and Reconstructive Surgery, Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China.
⁸ Department of Reproduction and Genetics, The First Affiliated Hospital of Kunming Medical University, 295 Xichang Road, Kunming, 650032, Yunnan Province, China. wanghuawei99@163.com.
⁹ Department of Reproduction and Genetics, The First Affiliated Hospital of Kunming Medical University, 295 Xichang Road, Kunming, 650032, Yunnan Province, China. tanglikm@163.com.

^# Contributed equally.

PMID: 40549330
DOI: 10.1007/s11547-025-02032-9

Abstract

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.

Keywords: Artificial intelligence; Digital healthcare; Polycystic ovary syndrome.

Publication types

Review

Grants and funding

No. 82160281/National Natural Science Fund of China