Artificial intelligence and assisted reproductive technology: A comprehensive systematic review - Yen-Chen Wu; Emily Chia-Yu Su; Jung-Hsiu Hou; Ching-Jung Lin; Krystal Baysan Lin; Chi-Huang Chen (Taiwanese Journal of Obstetrics & Gynecology)
The objective of this review is to evaluate the contributions of Artificial Intelligence (AI) to Assisted Reproductive Technologies (ART), focusing on its role in enhancing the processes and outcomes of fertility treatments. This study analyzed 48 relevant articles to assess the impact of AI on various aspects of ART, including treatment efficacy, process optimization, and outcome prediction. The effectiveness of different machine learning paradigms—supervised, unsupervised, and reinforcement learning—in improving ART-related procedures was particularly examined. The findings indicate that AI technologies significantly enhance ART processes by refining tasks such as embryo and sperm analysis and facilitating personalized treatment plans based on predictive modeling. Notable improvements were observed in the accuracy of diagnosing and predicting successful outcomes in fertility treatments. AI-driven models provided more precise forecasts of the optimal timing for clinical interventions such as egg retrieval and embryo transfer, which are critical to the success of ART cycles. The integration of AI into ART represents a transformative advancement, substantially improving the precision and efficiency of fertility treatments. The continuous evolution of AI methodologies is likely to further revolutionize this field, enabling more tailored and successful treatment approaches. AI is becoming an indispensable tool in reproductive medicine, enhancing both the effectiveness of treatments and the clinical decision-making process. This review underscores the potential of AI to act as a catalyst for innovative solutions in the optimization of ART.