Non-Invasive and AI-Enabled Diagnostic Approaches for Polycystic Ovary Syndrome: A PRISMA-Guided Narrative Review

Abstract

Background Polycystic ovary syndrome (PCOS) is one of the most common endocrine disorders in women of reproductive age and is associated with infertility, obesity, insulin resistance, and elevated long-term to cardiovascular risk. Traditional diagnostics rely on biochemical examinations and pelvic ultrasonography imaging. These are resource-intensive, operator-dependent, and frequently inaccessible in low-resource settings. Given the pronounced heterogeneity of PCOS phenotypes, traditional single-modality diagnostics frequently fail, necessitating a shift toward multimodal diagnostic approaches. Objective To critically evaluate the diagnostic efficacy and clinical applicability of non-invasive and minimally invasive diagnostic approaches for PCOS, encompassing artificial intelligence (AI) and machine learning (ML) methods, ultrasonographic imaging, hormonal profiling, clinical and biochemical markers, and insulin resistance indicators. Methods A structured narrative review was conducted using Publish or Perish (Version 8.17.4863.9118) across PubMed and Google Scholar. Studies published between 2014 and 2025 in English-language peer-reviewed journals were considered eligible if they focused on PCOS detection or diagnosis and reported at least one non-invasive or minimally invasive diagnostic modality. After deduplication, 62 papers were selected from 1,110 retrieved. A PRISMA flow diagram is included to document the selection process transparently; this review does not claim formal systematic review compliance. Results AI-based diagnostic models have reported high accuracy in experimental settings for both image-based and data-driven PCOS detection. While specific architectures exceed 90% accuracy offering viable pathways for low-resource screening, real-world deployment remains constrained by a lack of dataset standardization. Automatic segmentation of ultrasound images has the potential to reduce inter-operator variability in antral follicle counting. Hormonal profiling, centred on LH/FSH ratio, anti-Müllerian hormone, and androgen panels, remains foundational, while emerging molecular, metabolomic, and metabolic biomarkers offer complementary phenotypic precision. Conclusion The evidence projects a diagnostic shift toward reproducible, multimodal, and phenotype-sensitive PCOS detection. Integration of AI-based pipelines when integrated with imaging morphology, hormonal data, and metabolic markers offers significant opportunities for equitable, early-stage identification across diverse healthcare settings. These findings support the development of accessible, scalable screening tools to reduce diagnostic delay and improve women’s reproductive and metabolic health outcomes globally, though standardised external validation and equity-centered design remain critical priorities.