Artificial Intelligence in Assisted Reproduction: Advances, Challenges and Perspectives

Authors

  • Priscila Luiza dos Santos Faculdade Santa Marcelina
  • Maria Eduarda Del Frari
  • Maria Eduarda Lacerda Barros Bessa
  • Anna Gabriella Venâncio Neves de Moraes Tosta
  • José Gerfeson Alves https://orcid.org/0000-0003-0364-3151

DOI:

https://doi.org/10.36557/2674-8169.2025v7n7p1421-1436

Keywords:

Artificial intelligence, Assisted reproduction, Infertility, Embryo selection, Health technology

Abstract

Objective: To evaluate the advantages and disadvantages of using artificial intelligence (AI) in assisted reproduction infertility treatments in assisted reproduction, highlighting its impact on the efficiency and accuracy of procedures.

Method: This is a narrative literature review. Articles were selected from PubMed, VHL and Connected Papers, published between 2014 and 2024, using descriptors such as “artificial intelligence”, ‘assisted reproduction’ and ‘fertility treatment’.

Results: AI has been shown to improve embryo image analysis, increasing the success rate in selecting viable embryos, as well as optimizing diagnoses and clinical decisions. Our analysis covered the advances, challenges and prospects for the application of this technology. However, limitations such as high cost, lack of regulation and ethical challenges related to the use of AI were also identified.

Final considerations: The use of artificial intelligence in assisted reproduction has potential to revolutionize clinical practice, with significant benefits for the efficiency of treatments. However, it is necessary to balance technological advances with

ethical issues and equitable access for patients.

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References

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Published

2025-07-25

How to Cite

dos Santos, P. L., Del Frari , M. E., Lacerda Barros Bessa , M. E., Venâncio Neves de Moraes Tosta , A. G., & Alves, J. G. (2025). Artificial Intelligence in Assisted Reproduction: Advances, Challenges and Perspectives. Brazilian Journal of Implantology and Health Sciences, 7(7), 1421–1436. https://doi.org/10.36557/2674-8169.2025v7n7p1421-1436