Please ensure that the Reply includes more than 200 words with scholarly articles, and the plagiarism level must remain below 20%. Improving fertility oucomes with AI We are in an era of medicine where technological advances increasingly impress us. In the field of reproduction and fertility, genetic techniques are currently being used to predict embryo health and significantly improve pregnancy quality, its normal development, and its successful outcome—ultimately what parents hope for and long for. Currently, technological advancements have reached a level where it is possible to study embryos created through in vitro fertilization and identify chromosomal abnormalities before implantation. This is undoubtedly an extraordinary step forward in modern reproductive medicine, as until now, such abnormalities have been the main causes of implantation failure and early pregnancy loss. Several studies have explored how artificial intelligence and advanced genetic analysis can help identify embryos with the highest probability of successful development. Traditionally, embryo evaluation in IVF relies heavily on visual assessment by embryologists, who examine factors such as cell division patterns and overall embryo morphology. However, these assessments can sometimes be subjective and vary among geneticists. New computational approaches are seeking to improve the process. Artificial intelligence is at the forefront of studying and analyzing hundreds and thousands of embryos, using its algorithms to identify clinical variables that reveal healthy or unhealthy patterns that would be very difficult to observe with the naked eye. The accuracy of these patterns is approximately 70%, and they combine non-invasive techniques with the provided clinical information (these techniques are still under study, development, and approval). It is crucial to clarify that these technological advances are used in a complementary manner, and nothing can replace the evidence of scientific research. Currently, preimplantation genetic testing (PGT-A) often requires obtaining a small number of embryonic cells for analysis, which increases costs and invasive procedures. With the use of artificial intelligence, we are seeing a significant reduction in this need, and clinically, this translates into improved selection of the quality of viable embryos that can result in a successful pregnancy and a happy family. I would like to cite the example of BELA (Blastocyt Evaluation Learning Algorithm), developed by a team of researchers at Weill Cornell Medicine in New York, a pioneering center in this type of study. BELA can determine whether an embryo is euploid (normal) or aneuploid (abnormal), a key factor in the development and completion of a successful pregnancy (Barnes et al., 2023).For us as advanced practice nurses (APNFs), it is vital to stay abreast of the new tools emerging in reproductive medicine and fertility, as this allows us to significantly improve the quality of our clinical care and provide accurate information to our patients. In conclusion, I can say that the analysis and study of genetic research techniques assisted by Artificial Intelligence, a tool that is so fashionable and prevalent in all aspects of life, promises great advances and achievements in this branch of clinical research. Clearly, these techniques are new and require much deeper study so that, in the short term, we can make them available precisely to provide better comprehensive care to our patients who seek... complete success in their reproductive health. References: Barnes, J., Hajirasouliha, I., & Elemento, O. (2022). Artificial intelligence for embryo selection in in vitro fertilization. The Lancet Digital Health, 4(12), e893–e902. Barnes, J., Brendel, M., Gao, V. R., Rajendran, S., Kim, J., Li, Q., Li, Y., Le, T., Nguyen, P., Patel, A., Reddy, S., Singh, R., Smith, A., Tan, W., Wang, H., Zhang, L., & Hajirasouliha, I. (2023). A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: A retrospective model development and validation study. The Lancet Digital Health, 5(1), e28–e40. https://doi.org/10.1016/S2589-7500(22)00200-9
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