AI In Polycystic Ovary Syndrome: Revolutionizing Diagnosis And Management

Abstract

Polycystic Ovary Syndrome (PCOS) is an endocrine disorder common among 4–20% of women in reproductive age. Reproductive, metabolic, and psychological implications of the syndrome are high. Present approaches for diagnosing PCOS have several issues associated with subjectivity, variability, and delayed diagnosis through clinical examination, biochemical investigations, and imaging. The rise of AI technology has made it a game changer in dealing with the problems related to diagnosis and provides enhanced diagnostic precision and speed through ML and DL models. This paper reviews the contribution of AI technologies in PCOS prediction and diagnosis. AI models, supervised as well as unsupervised, perform better than conventional approaches with large, complex data sets. Random forests and ANNs have reached high diagnostic accuracies over 90%, and patients are clustered into phenotypes for treatment tailored by unsupervised clustering techniques. They embrace clinical, genetic and imaging data with non-invasive diagnosis, early diagnoses and cost effective interventions. SHAP and LIME are two other forms of AI structures of explanatory methods for added model explainability and increased clinician trust. When it comes to prediction of risk of developing PCOS by using AI the genetic factors, lifestyle parameters, and a family history of PCOS is used accurately up to 96 percent. Innovations in diagnostics include analysis of ultrasound images coupled with identification of biomarkers using AI including hormonal concentrations and ovarian follicles, which combine to boost reliability, particularly within clinical or resource scarce environments. Some concerns are yet to be faced; these are the scarcity of unified and varied databases, problem of heterogeneity of diagnosis procedures, data protection issues, and prejudicial AI systems. Challenges of these limitations include; Future directions entail creating models that can be explained, combining various data input for comprehensive diagnosis, and controlling big, detailed databases. In a syncronized form, AI from wearable to portable health applications will be foreshadow to follow patients by engaging them in real time for better treatment. To offer solutions for healthcare that originate from artificial intelligence and that are honest and fair, ethical and regulatory issues have to be resolved. The strength of this review is to use AI to predict the diagnosis and treatment of PCOS for personalized, efficient, and accessible medical care, and several directions for improvement.