Artificial intelligence in polycystic ovary syndrome: a systematic review of diagnostic and predictive applications

Polycystic ovary syndrome (PCOS) is one of the most common endocrine disorders, affecting 8–13% of women of reproductive age. Its heterogeneous presentation and the variability of diagnostic criteria make accurate diagnosis and effective management challenging. Artificial intelligence (AI) methods, including machine learning (ML), deep learning (DL), explainable AI (XAI), and large language models (LLMs), have recently emerged as promising approaches to address these gaps. This systematic review aimed to provide a comprehensive synthesis of AI applications in PCOS, with emphasis on diagnostic performance, biomarker discovery, risk prediction, clinical decision support, model interpretability, and the emerging use of generative AI. Following PRISMA 2020 guidelines, PubMed, Scopus, and Web of Science were searched from inception to March 2025. Eligible studies applied AI techniques to PCOS and reported at least one performance metric. Two reviewers independently screened and extracted data, with quality appraisal conducted using QUADAS-2 and ROBIS. Given the heterogeneity of designs and outcomes, findings were narratively synthesized across imaging, clinical/EHR, and biomarker/-omics domains. From 662 retrieved records, 80 studies met the inclusion criteria. CNN-based models dominated imaging applications, with accuracies often exceeding 95% and occasionally reaching 98–99%. Supervised ML approaches, particularly random forests and support vector machines, achieved consistent high performance in clinical and biochemical datasets. Omics-based studies revealed novel biomarkers such as HDDC3, SDC2, MAP1LC3A, and OVGP1. However, only about one-quarter of studies applied XAI methods, limiting transparency and clinical trust. Early evaluations of LLMs suggested potential for patient education and decision support but highlighted risks of bias, hallucination, and lack of domain-specific training. Key limitations across studies included small sample sizes, class imbalance, methodological heterogeneity, and limited external validation. AI offers substantial opportunities to advance PCOS diagnosis and prediction by integrating multimodal data and reducing diagnostic subjectivity. Yet its clinical adoption is constrained by interpretability gaps and insufficient validation. Future priorities include large multicenter studies, standardized reporting, systematic use of XAI, and careful evaluation of LLMs to ensure safe, equitable, and clinically meaningful integration into PCOS care. CNN-based imaging models and supervised ML classifiers consistently outperformed traditional diagnostic criteria, reducing subjectivity in ultrasound and clinical assessment. Omics-driven studies identified novel candidate genes (HDDC3, SDC2, MAP1LC3A, OVGP1) with potential for risk stratification. Only ~25% of studies applied XAI methods (SHAP, LIME, Grad-CAM). Where used, these improved interpretability and clinician confidence, but most models remained opaque. LLMs (ChatGPT, BERT, Gemini) are emerging tools for patient communication, clinical note summarization, and literature synthesis, but raise concerns about hallucination, bias, and lack of domain-specific training. QUADAS-2 and ROBIS assessments revealed frequent issues with patient selection, dataset representativeness, and lack of external validation. Kaggle datasets were overused, reducing generalizability. Future work should focus on multicenter collaborations, multimodal integration, routine incorporation of XAI, and robust evaluation of LLMs. AI has the potential to enhance early diagnosis and personalized management of PCOS, but adoption will depend on reproducibility, transparency, and clinician trust rather than accuracy alone.