Artificial Intelligence for Prediction and Early Detection of Polycystic Ovary Syndrome: A Review

Abstract

Polycystic ovary syndrome (PCOS) is one of the commonest endocrine disorders in women of reproductive age, yet diagnosis is often delayed because symptoms are varied and routine care may fail to integrate menstrual history, androgen excess, laboratory findings, and ovarian imaging. Earlier recognition matters because PCOS affects fertility, metabolic health, pregnancy, psychological well-being, and quality of life. AI has therefore attracted growing interest in prediction and early detection. Relevant tools include machine-learning models for structured clinical data, deep-learning systems for ultrasound images, natural-language processing of free-text reports, and digital platforms that generate risk scores from symptoms or menstrual-tracking data. Published studies suggest promise in symptom-based triage, EHR case finding, ultrasound standardization, and emerging omics or mobile-health approaches. At the same time, literature has important weaknesses. Many studies are retrospective, single-center, or based on small, curated datasets. Diagnostic definitions are not always consistent, external validation is limited, and only a minority of studies explain how predictions would be used in real clinical practice. There are also age-specific concerns: adult criteria cannot simply be transferred to adolescents, because current guidance does not support the use of ovarian morphology or anti-mullerian hormone (AMH) as diagnostic criteria in adolescents. At present, AI should be seen as an aid to earlier case finding, triage, and standardized interpretation rather than as a replacement for clinical judgment. The most useful future systems will be the ones that are clinically understandable, fair across patient groups, validated in diverse settings, and tested in routine care pathways. Eastern Med Coll J. January 2026 Vol.11 No.1 : 82-92