Harnessing the Power of Artificial Intelligence to Optimize Efficiency and Improve Clinical Outcomes in Ovarian Stimulation for Fertility Treatments

Abstract

Ovarian stimulation is a crucial element of artificial reproductive technology (ART) necessary for the success of fertility treatments. The adoption of ovarian stimulation has not fully stabilized the procedure due to its strong dependence on various biological and external factors. This analysis explores how Artificial Intelligence (AI) and Machine Learning (ML) technologies can improve the efficiency of ovarian stimulation protocols while also enhancing their outcomes.

Methods: The study examines the application of AI/ML software in ovarian stimulation by conducting an exhaustive literature review to demonstrate how these innovations improve the tailoring of treatments, reduce risks, and increase success rates. The research incorporated peer-reviewed articles published between January 2015 and August 2024, utilizing three databases: PubMed, Scopus, and IEEE Xplore.

Results: The use of AI and ML algorithms in ovarian stimulation has demonstrated effectiveness by analyzing large sets of medical data to predict patient responses to various methods. Tailoring dosage and protocol choices using these technologies results in noticeably improved clinical outcomes. AI systems currently track ovarian response in conjunction with follicular development and hormone levels, allowing for predictive models that enhance treatment efficiency.

Conclusion: Artificial intelligence (AI) and machine learning (ML) are revolutionizing ovarian stimulation practices. Current research indicates significant enhancements in treatment effectiveness and patient results, yet there are challenges to successfully incorporating these technologies into routine clinical environments. Further scientific research needs to prioritize data security, algorithm equity, and the creation of standardized protocols to ensure both safety and efficacy in the application of AI.