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作者

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

作者单位

⁷Department of Plastic and Reconstructive Surgery, Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China ²Department of Gynecological Endocrinology, Jinan Maternity and Child Care Hospital Affiliated to Shandong First Medical University, Jinan, 250001, China ⁶Clinical Anatomy & Reproduetive Medieine Application Institute, Hengyang Medieal Sehool, University of South China, Hengyang, China ³Department of Dermatology, The Second Affiliated Hospital of Kunming Medical University, Kunming, 650101, Yunnan, China ¹Department of Reproduction and Genetics, The First Affiliated Hospital of Kunming Medical University, 295 Xichang Road, Kunming, 650032, Yunnan Province, China ⁵Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200032, China ⁴Key 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

刊名

La radiologia medica

年份

2025

卷号

Vol.130 No.9

页码

1409-1441

关键词

Artificial intelligence Polycystic ovary syndrome Digital healthcare

摘要

Background Integrating artificial intelligence prospected in the practical clinical management of polycystic ovary syndrome 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, an...更多

Background Integrating artificial intelligence prospected in the practical clinical management of polycystic ovary syndrome 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.收起

文献类型

期刊