Clinical ethical evaluatıon of the use of artificial intelligence in reproductive technology applications
Articles > Article · 2026
Authors: Zeynep Konukçu Mehdili, Çağatay ÜSTÜN, Seçil Özçiftçi
Ege Tıp Dergisi, National Refreed University Journal, International, TR DİZİN (ULAKBİM), 2026, vol 65, pp 349-356
DOI: 10.19161/etd.1789189
Summary
Aim: The objective of this article is to examine the use of artificial intelligence technologies in infertility treatments—particularly in the IVF process—highlight the contributions that AI integration can offer, and evaluate the ethical and legal dimensions of these processes.Materials and Methods: This study reviewed the use of AI and machine learning in infertility treatments, especially in the IVF process, through an examination of the literature and current clinical practices. The diversity of data used to train algorithms in IVF applications, as well as their integration with advanced reproductive technologies such as PGT and MRT, was addressed. This study is a qualitative literature review, and research published between 2015 and 2025 in the PubMed, Web of Science, Scopus, and Google Scholar databases was examined. The keywords ‘artificial intelligence,’ ‘IVF,’ ‘PGT,’ ‘machine learning,’ ‘embryo selection,’ and ‘ethics’ were combined during the search process. Inclusion criteria were: full-text availability in English or Turkish, and experimental, clinical, or ethics-focused studies examining the relationship between AI and IVF. Exclusion criteria were: editorial articles, short commentary letters, and opinion pieces without empirical data.Results: The use of AI and machine learning in the IVF process has the potential to increase objectivity in embryo selection and improve success rates. The diversity of data used in training algorithms can prevent uniform embryo selection, promoting fairer and more inclusive outcomes. However, due to the current lack of a standardised database, ethical concerns and legal liabilities arise.Conclusions: Artificial intelligence applications hold promising potential in infertility treatments. However, the use of AI in IVF and related reproductive technologies, constrained by non-standardised databases, entails ethical and legal risks. Therefore, it is essential to incorporate the broadest and most diverse data sets possible when training algorithms to ensure both technical accuracy and ethical fairness. Additionally, the ethical dimensions of AI applications in PGT, MRT, and special reproductive scenarios must be carefully evaluated.