{"title":"人工智能在多囊卵巢综合征治疗中的应用：过去、现在和未来。","authors":"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","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Integrating artificial intelligence (AI) prospected in the practical clinical management of polycystic ovary syndrome (PCOS) promised significant improvement in efficiency, interpretability, and generalizability.</p><p><strong>Purpose: </strong>To delineate a comprehensive inventory of AI-driven interventions pertinent to PCOS across diverse clinical contexts.</p><p><strong>Evidence reviews: </strong>AI-based analytics profoundly transformed the management of PCOS, particularly in the domains of prediction, diagnosis, classification, and screening of potential complications.</p><p><strong>Results: </strong>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><p><strong>Conclusion: </strong>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>","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":"1409-1441"},"PeriodicalIF":4.8000,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12454626/pdf/","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence in polycystic ovarian syndrome management: past, present, and future.\",\"authors\":\"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\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Integrating artificial intelligence (AI) prospected in the practical clinical management of polycystic ovary syndrome (PCOS) promised significant improvement in efficiency, interpretability, and generalizability.</p><p><strong>Purpose: </strong>To delineate a comprehensive inventory of AI-driven interventions pertinent to PCOS across diverse clinical contexts.</p><p><strong>Evidence reviews: </strong>AI-based analytics profoundly transformed the management of PCOS, particularly in the domains of prediction, diagnosis, classification, and screening of potential complications.</p><p><strong>Results: </strong>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><p><strong>Conclusion: </strong>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>\",\"PeriodicalId\":20817,\"journal\":{\"name\":\"Radiologia Medica\",\"volume\":\" \",\"pages\":\"1409-1441\"},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2025-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12454626/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Radiologia Medica\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s11547-025-02032-9\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/6/23 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Radiologia Medica","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s11547-025-02032-9","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/6/23 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Artificial intelligence in polycystic ovarian syndrome management: past, present, and future.
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.
期刊介绍：
Felice Perussia founded La radiologia medica in 1914. It is a peer-reviewed journal and serves as the official journal of the Italian Society of Medical and Interventional Radiology (SIRM). The primary purpose of the journal is to disseminate information related to Radiology, especially advancements in diagnostic imaging and related disciplines. La radiologia medica welcomes original research on both fundamental and clinical aspects of modern radiology, with a particular focus on diagnostic and interventional imaging techniques. It also covers topics such as radiotherapy, nuclear medicine, radiobiology, health physics, and artificial intelligence in the context of clinical implications. The journal includes various types of contributions such as original articles, review articles, editorials, short reports, and letters to the editor. With an esteemed Editorial Board and a selection of insightful reports, the journal is an indispensable resource for radiologists and professionals in related fields. Ultimately, La radiologia medica aims to serve as a platform for international collaboration and knowledge sharing within the radiological community.
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