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Artificial intelligence is transforming reproductive medicine by enabling more nuanced and personalized interpretation of key biomarkers such as anti-Müllerian hormone (AMH) and inhibin B. While AMH is widely adopted for assessing ovarian reserve, inhibin B-an follicle-stimulating hormone (FSH)-dependent marker of follicular activity-has been historically underutilized due to its dynamic nature and lack of standardized assays. In this review, we explore how AI-driven tools can integrate these hormonal signals to improve clinical decision-making. We highlight three representative models: OvaRePred (also known as HerTempo), which predicts ovarian reserve and perimenopausal timing; PCOSt, which enables early screening and phenotyping of polycystic ovary syndrome; and POvaStim, which personalizes gonadotropin dosing by modeling ovarian sensitivity. Collectively, these tools offer new possibilities for individualized fertility management and may help shift reproductive care from reactive treatment toward proactive, data-informed health planning. We also discuss emerging innovations such as cross-platform assay harmonization, point-of-care hormone testing, and longitudinal biomarker modeling. Looking ahead, integration with nutritional interventions, wearable technologies, and genetic or immunologic data could extend these tools beyond assisted reproduction-supporting a broader, AI-enabled ecosystem for women's lifelong health.
Xu et al. (Mon,) studied this question.