Artificial Intelligence in Reproductive Medicine: Transforming Diagnosis, Treatment, and IVF Outcomes

Artificial Intelligence (AI) is rapidly transforming assisted reproductive technologies (ART) by enhancing diagnostic accuracy, treatment personalization, and clinical decision-making. AI-driven tools can predict embryo viability, optimize sperm selection in real time, and tailor ovarian stimulation protocols to individual patient characteristics. Emerging applications, such as convolutional neural networks for embryo imaging and machine learning models for sperm analysis, reduce subjectivity and improve the reproducibility of outcomes. Reinforcement learning and computer vision are also advancing stimulation regimens and laboratory automation, while robotic-assisted interventions benefit from greater precision. Despite these innovations, important challenges remain. Clinical adoption is limited by heterogeneity of training datasets, algorithmic bias, and a lack of transparency in model design. Ethical concerns—including data privacy, informed consent, and equitable access and potential health risks—further complicate integration into fertility care. Moreover, financial and infrastructural barriers restrict widespread implementation, particularly in resource-limited settings. This review highlights current computational approaches, evaluates their contributions to embryo and gamete assessment, ovarian stimulation, and surgical procedures, and discusses unresolved issues of validation and regulation. Ultimately, AI holds the potential to make ART more efficient, precise, and patient-centred. Still, it’s safe and equitable integration will require interdisciplinary collaboration, robust clinical validation, and adherence to ethical and regulatory standards.