Artificial intelligence in ovarian pathophysiology and management: a systematic review and meta-analysis
Abstract
Background: The integration of artificial intelligence (AI) into reproductive medicine and gynecologic oncology has driven transformative advances in the diagnosis, prognosis, and management of ovarian conditions, including malignancies, endocrine disorders, and functional impairments.
Objective: This PRISMA 2020-compliant systematic review and meta-analysis synthesizes evidence from 81 peer-reviewed studies to evaluate the efficacy, clinical applications, and limitations of AI and machine learning (ML) models across the full spectrum of ovarian health and disease.
Methods: A systematic literature search was conducted across PubMed/MEDLINE, Embase, Web of Science, Cochrane Library, and Scopus for studies published between January 2015 and January 2026. Eligible studies included the development, validation, or clinical application of AI/ML models for ovarian pathophysiology or management. Study quality was appraised using the QUADAS-2 tool for diagnostic accuracy studies and the PROBAST tool for predictive modeling studies. Narrative synthesis and random-effects meta-analysis were performed, with pre-specified subgroup analyses, meta-regression, and sensitivity analyses to explore heterogeneity. Applications were categorized into ovarian cancer (diagnosis, staging, prognosis, treatment guidance), reproductive endocrinology (ovarian reserve, Polycystic Ovary Syndrome (PCOS), ovarian stimulation), and fundamental ovarian biology.
Results: AI models demonstrated high diagnostic…
How strong is the evidence?
Evidence Hub reads this as a meta-analysis, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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