Artificial Intelligence in Routine IVF Practice

Background: Artificial Intelligence (AI) has emerged as a transformative tool in in vitro fertilization (IVF) as it has done in other sectors. In IVF, AI offers advancements in embryo selection, treatment personalization, and outcome prediction. It does so by leveraging deep learning and computer vision, as well as AI-driven platforms such as ERICA, iDAScore, and IVY where the goal is to address the limitations of traditional embryo assessment. Key amongst them are the issues of subjectivity, labor intensity, and limited predictive power. Despite rapid technological progress, the integration of AI into routine IVF practice faces key challenges. These are issues related to clinical validation, ethical dilemmas, and workflow adaptation. Rationale/Objectives: This review synthesizes current evidence to evaluate the role of AI in IVF, focusing on six critical dimensions: (1) the evolution of AI from traditional embryology to algorithmic assessment, (2) clinical validation and regulatory considerations, (3) limitations and ethical challenges, (4) pathways for clinical integration, (5) real-world applications and outcomes, and (6) future directions and policy recommendations. The objective is to provide a comprehensive roadmap for the responsible adoption of AI in reproductive medicine. Outcomes: AI demonstrates significant potential to improve the precision and efficiency of IVF. Studies report that AI models can achieve 10 to 25% higher accuracy in predicting embryo viability and implantation potential compared to traditional morphological assessment by embryologists. This enhanced predictive power supports more consistent embryo ranking, facilitates elective single-embryo transfer (eSET) strategies, and is associated with 30 to 50% reductions in embryologist workload per embryo cohort. Early adopters report promising trends. However, large-scale randomized controlled trials have yet to conclusively demonstrate a statistically significant increase in live birth rates per transfer compared to expert embryologist selection. The most immediate and evidenced value of AI lies in hybrid decision-making models. This is where it augments embryologists by providing data-driven, objective support, thereby standardizing workflows and reducing subjectivity. Wider Implications: The sustainable integration of AI into IVF banks on three key aspects: robust evidence generation, interdisciplinary collaboration, and global standardization. To foster these, policymakers ought to establish regulatory frameworks for transparency and bias mitigation. On their part, clinicians need training to interpret AI outputs critically. Ethically, safeguarding patient trust and equity is non-negotiable. Future innovations, mainly AI-enhanced genomics and real-time monitoring, could further personalize care. However, their success depends on addressing current limitations. By balancing innovation with ethical vigilance, AI holds the potential to revolutionize IVF while upholding the highest standards of patient care.