LAUSR

Abstract In the recent years a special attention has been given to a major health concern namely to male infertility, defined as the inability to conceive after 12 months of regular unprotected sexual intercourse, taken into account the statistics that highlight that sperm counts have dropped by 50–60% in recent decades. According to the WHO, infertility affects approximately 9% of couples globally, and the male factor is believed to be present in roughly 50% of cases, with exclusive responsibility in 30%. The aim of this article is to present an evidence-based approach for diagnosing male infertility that includes finding new solutions for diagnosis and critical outcomes, retrieving up-to-date studies and existing guidelines. The diverse factors that induce male infertility generated in a vast amount of data that needed to be analyzed by a clinician before a decision could be made for each individual. Modern medicine faces numerous obstacles as a result of the massive amount of data generated by the molecular biology discipline. To address complex clinical problems, vast data must be collected, analyzed, and used, which can be very challenging. The use of artificial intelligence (AI) methods to create a decision support system can help predict the diagnosis and guide treatment for infertile men, based on analysis of different data as environmental and lifestyle, clinical (sperm count, morphology, hormone testing, karyotype, etc.), and “omics” bigdata. Ultimately, the development of AI algorithms will assist clinicians in formulating diagnosis, making treatment decisions, and predicting outcomes for assisted reproduction techniques. Summary Sentence The article intends to present an evidence-based approach for the diagnosis of male infertility. These involve many molecular aspects and the necessity of artificial intelligence techniques for diagnosis prediction and treatment guidance. Graphical Abstract