Fertility potential ever more diminishes due to the complex, multifactorial, and still not entirely clarified process of reproductive aging in women and men. Gamete quality and reproductive lifespan are compromised by biologic factors like mitochondrial dysfunction, increased oxidative stress (OS), and incremental telomere shortening. Clinically confirmed biomarkers, including follicle-stimulating hormone (FSH) and anti-Müllerian hormone (AMH), are used to estimate ovarian reserve and reproductive status, but these markers have limited predictive validity and an incomplete representation of the complexity of reproductive age. Recent advances in artificial intelligence (AI) have the capacity to address the integration and interpretation of disparate and complex sets of data, like imaging, molecular, and clinical, for consideration. AI methodologies that improve the accuracy of reproductive outcome predictions and permit the construction of personalized treatment programs are machine learning (ML) and deep learning. To promote fertility evaluations, here, as part of its critical discussion, the roles of mitochondria, OS, and telomere biology as latter-day biomarkers of reproductive aging are presented. We also address the current status of AI applications in reproductive medicine, promises for the future, and applications involving embryo selection, multi-omics set integration, and estimation of reproductive age. Finally, to ensure that AI technology is used ethically and responsibly for reproductive care, model explainability, heterogeneity of data, and other ethical issues remain as residual concerns.
Moustakli et al. (Tue,) studied this question.