Introduction: In the ever-evolving landscape of healthcare, a remarkable ally has emerged to investigate the cause and treatment of infertility,name is artificial intelligence (AI). Artificial intelligence (AI) can assist in female infertility treatment in several ways, for example :Determining the best treatment for individual infertility patients: Reproductive experts can use AI and machine learning models to determine the most appropriate therapy for infertility patients, increasing successful pregnancy rates and reducing the financial burden.Improving infertility diagnosis and ART outcomes: AI has the potential to improve infertility diagnosis and ART outcomes, estimated as pregnancy and/or live birth rate, especially with recurrent infertility cases.Enhancing in vitro fertilization (IVF) procedures: AI-assisted IVF is a rapidly growing area of research that can improve ovarian stimulation outcomes and efficiency by optimizing the dosage and timing of medication. AI can also help women find good quality eggs to support IVF and egg freezing. AI can assess embryo quality and select the highest quality embryos for fertilization. AI can consistently and effectively identify an embryo with optimal developmental and implantation potential.Predicting pregnancy outcomes: AI can predict the chances of pregnancy in IVF cycles, guiding treatment and reducing the burden of patients.Developing a web-based system for predicting IVF outcome: A proposed web-based system using artificial intelligence can predict IVF outcome by including majority indicators of IVF cycle assessment, such as stimulation protocol, gonadotrophin dose, hormone level, and couple's information.
Methods: The method of this review study was carried out by investigating the articles available in ISI, Scopus, PubMed and Google Scholar .
Results: In summary, AI can assist in female infertility treatment by determining the best treatment for individual patients, improving infertility diagnosis and ART outcomes, enhancing IVF procedures, predicting pregnancy outcomes, and developing a web-based system for predicting IVF outcome
Conclusion: Artificial intelligence (AI) is being increasingly used in the field of infertility treatment, particularly in in vitro fertilization (IVF) procedures. AI is being tested in several areas of reproductive medicine, including sperm identification and morphology, automatic embryo cell stage prediction, embryo evaluation, and prediction of live birth, as well as the development of improved stimulation protocols. AI technology has the potential to improve infertility diagnosis and ART outcomes, estimated as pregnancy and/or live birth rate, especially with recurrent infertility cases. AI can also help expert find good quality eggs to support IVF and egg freezing. AI-assisted IVF is a rapidly growing area of research that can improve ovarian stimulation outcomes and efficiency by optimizing the dosage and timing of medication. AI can also help determine the best treatment for individual infertility patients, increasing successful pregnancy rates and reducing financial burden. AI can assess embryo quality and select the highest quality embryos for fertilization. AI can consistently and effectively identify an embryo with optimal developmental and implantation potential. AI has the potential to enhance the IVF process at every step where decisions are made, and it could potentially be used to help doctors and patients make better-informed choices about additional IVF cycles and oocyte freezing.