چکیده مقاله Artificial Intelligence for Advancing Infertility Treatments: A systematic review
Infertility may be a developing worldwide concern, influencing millions of couples around the world. Recent advancements in Artificial Intelligence (AI) have introduced innovative approaches to addressing infertility challenges, offering personalized care and improving the success rates of fertility treatments such as In Vitro Fertilization (IVF). The main purpose of this study is to review the effectiveness and efficiency of Artificial Intelligence in improving infertility treatments. Particularly, it points to investigate how AI-powered advances are utilized to anticipate IVF victory rates, diminish complications, and improve patient care. Also, the study will look at the potential of AI to offer personalized treatment plans based on personal patient information, and its part in progressing regenerative medication. The article explores the application of artificial intelligence (AI) models to predict pregnancy outcomes in IVF treatments. A primary goal was to improve decision-making by leveraging AI for disease management and outcome prediction. The study demonstrated that AI-based tools could enhance the accuracy of predicting success rates, potentially reducing the risk of multiple pregnancies and increasing efficiency in patient care. Furthermore, the implementation of AI in reproductive healthcare has led to a more individualized approach to patient treatment, with better data integration and a reduction in human error. The findings indicate that AI models can provide accurate and useful predictions in IVF treatments, with positive outcomes reported in approximately 90% of reviewed cases. However, the study also highlights the need for further improvements to address the limitations of current AI systems, particularly in terms of integrating diverse patient data and ensuring ethical considerations in AI deployment. Overall, AI holds significant potential to revolutionize the field of reproductive medicine and improve patient outcomes.