Leveraging Artificial Intelligence to Enhance Efficiency and Outcomes in Ovarian Stimulation

Abstract

Artificial reproductive technology (ART) needs ovarian stimulation as its essential component to achieve fertility treatment success. The widespread implementation of ovarian stimulation has not stabilized the process because it depends heavily on multiple biological and external conditions. The analysis investigates how Artificial Intelligence (AI) and Machine Learning (ML) tools can boost ovarian stimulation protocol efficiency alongside enhancing their results.

Methods: The research analyses AI/ML software implementation in ovarian stimulation through complete literature review to show how these advancements boost treatment customization while lowering risks and enhancing outcome rates. Research included peer-reviewed publications within the January 2015 to August 2024 timeframe using the three databases: PubMed, Scopus, and IEEE Xplore.

Results: The application of AI and ML algorithms in ovarian stimulation has proven effective through analysis of extensive medical data to forecast how patients respond to different approaches. The individualization of dosages with protocol selections through these technologies leads to significantly better clinical results. AI systems now monitor ovarian response together with follicular growth alongside hormone levels which enables prediction models that improve treatment efficiency.

Conclusion:
AI and ML represent transformative forces in the field of ovarian stimulation. The existing evidence shows strong improvements regarding treatment efficiency and patient outcomes but integration obstacles exist for implementing these technologies in everyday clinical settings. Additional scientific investigation must focus on data protection alongside algorithm fairness along with standardized protocol development to guarantee both safety and success from AI implementations in Assisted Reproductive Technology.