Vaageessan Masilamani, Ragothaman Bharathyuvaraj, Velayudam Ramalingam Elangovan, Hema Ramji, Mohanraj Subramanian, Murthy Saravanan, Devaraj Jothimani, Fenn Moses, Mani Megalai, Venu Gopal, Samuel Duraivel
{"title":"Leveraging artificial intelligence for advancements in reproductive health.","authors":"Vaageessan Masilamani, Ragothaman Bharathyuvaraj, Velayudam Ramalingam Elangovan, Hema Ramji, Mohanraj Subramanian, Murthy Saravanan, Devaraj Jothimani, Fenn Moses, Mani Megalai, Venu Gopal, Samuel Duraivel","doi":"10.29063/ajrh2024/v28i11.21","DOIUrl":null,"url":null,"abstract":"<p><p>We are writing to address the growing interest in the role \nof artificial intelligence (AI) within healthcare, \nparticularly in the field of reproductive health. As \ntechnology continues to evolve, AI offers an \nunprecedented opportunity to transform how we \ndiagnose, treat, and improve access to reproductive \nservices, especially in underserved communities. AI-driven tools, supported by machine learning and big data \nanalytics, are already demonstrating their potential in \nenhancing outcomes in reproductive health. These tools \ncan predict fertility outcomes with impressive accuracy, \noptimize in vitro fertilization (IVF) success rates, and \nidentify early signs of reproductive disorders, such as \nendometriosis, polycystic ovary syndrome (PCOS), and \novarian cancer. By analyzing biomarkers, medical \nhistories, and lifestyle factors, AI algorithms empower \nhealthcare providers to deliver personalized and \neffective treatment plans tailored to individual needs.</p>","PeriodicalId":7551,"journal":{"name":"African journal of reproductive health","volume":"28 11","pages":"216-217"},"PeriodicalIF":0.7000,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"African journal of reproductive health","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.29063/ajrh2024/v28i11.21","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
引用次数: 0
Abstract
We are writing to address the growing interest in the role
of artificial intelligence (AI) within healthcare,
particularly in the field of reproductive health. As
technology continues to evolve, AI offers an
unprecedented opportunity to transform how we
diagnose, treat, and improve access to reproductive
services, especially in underserved communities. AI-driven tools, supported by machine learning and big data
analytics, are already demonstrating their potential in
enhancing outcomes in reproductive health. These tools
can predict fertility outcomes with impressive accuracy,
optimize in vitro fertilization (IVF) success rates, and
identify early signs of reproductive disorders, such as
endometriosis, polycystic ovary syndrome (PCOS), and
ovarian cancer. By analyzing biomarkers, medical
histories, and lifestyle factors, AI algorithms empower
healthcare providers to deliver personalized and
effective treatment plans tailored to individual needs.
期刊介绍:
The African Journal of Reproductive Health is a multidisciplinary and international journal that publishes original research, comprehensive review articles, short reports, and commentaries on reproductive heath in Africa. The journal strives to provide a forum for African authors, as well as others working in Africa, to share findings on all aspects of reproductive health, and to disseminate innovative, relevant and useful information on reproductive health throughout the continent.