Assisted Reproductive Technology is undergoing a major transformation with the introduction of artificial intelligence (AI), which is reshaping how medical treatments are carried out. In IVF, one of the persistent challenges has been maximizing the number of oocytes retrieved while efficiently managing clinical workload-particularly by reducing weekend procedures-without compromising outcomes. Although a patient's response may vary between cycles, evidence shows that adjusting the trigger day by one day does not significantly affect clinical results, enabling more flexible scheduling.
AI enables a shift from standardized protocols to personalized treatments, improving clinical outcomes, streamlining processes and enhancing operational efficiency. Recent research shows that AI-based models can optimize ovarian stimulation, improve trigger-day selection, and increase the number of fertilized oocytes compared to decisions made solely by physicians. AI algorithms have also accurately predicted the number of oocytes retrieved, contributing to more effective protocols and higher live birth rates.
Beyond trigger timing, AI has been shown to improve workflow efficiency in IVF clinics by optimizing monitoring schedules and balancing clinical workload without negatively affecting cycle outcomes.
Based on this growing evidence, a randomized controlled trial was designed to compare clinical outcomes of controlled ovarian stimulation when trigger and retrieval decisions are made solely by the physician versus when the physician is assisted by AI guidance.
