Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Upreti, Kamal; George, Jossy; Upreti, Shitiz; Mahajan, Shubham Title A name given to the resource Polycystic Ovary Syndrome Diagnosis: The Promise of Artificial Intelligence for Improved Clinical Accuracy Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Biomedical and Pharmacology Journal;Volume;18;Issue;1;pp.353-372 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.13005/bpj/3093" target="_blank" rel="noreferrer noopener">https://doi.org/10.13005/bpj/3093</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105002833893?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105002833893?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Uttar Pradesh, Ghaziabad, India; George J., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Uttar Pradesh, Ghaziabad, India; Upreti S., Department of IT &amp; Management, Maharishi Markandeshwar (Deemed to be University), Haryana, Mullana, Ambala, India; Mahajan S., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Uttar Pradesh, Ghaziabad, India Description An account of the resource PCOS is an endocrine illness that affects 610% of women worldwide. It can cause a variety of symptoms, including irregular menstruation periods, ovarian cysts, and hyperandrogenism. Its lack of defined biomarkers, overlapping symptoms, and heterogeneity make diagnosis difficult. By studying hormone profiles, identifying patterns difficult to see with conventional approaches, and offering great precision and accuracy, AI and ML techniques are transforming diagnostic difficulties. Hybrid models in the list include SWISS-AdaBoost and ensemble learning techniques that have accuracies up to 98% enabling early diagnosis along with appropriate treatment strategies. Early detection by technologies such as AI will prevent significant health complications that are PCOS-related, such as infertility, type II diabetes, or cardiovascular diseases. This study depicts the transformative role of the application of AI in diagnosing cases of PCOS and highlights the possibility of facilitating clinical decision-making, reducing potential diagnostic delays, and enhancing improvements in patient outcomes. Future research should hence be directed towards the establishment of AI within healthcare with consideration of validation, reliability, and ethical considerations to maximize its use in clinical practice. 2025 Oriental Scientific Publishing Company. All rights reserved. Subject The topic of the resource Artificial Intelligence; Endocrine disorders; Hyperandrogenism; Infertility; Polycystic ovaries; Rotterdam Criteria Publisher An entity responsible for making the resource available Oriental Scientific Publishing Company Relation A related resource ISSN: 9746242; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online