ARTIFICIAL INTELLIGENCE IN GENETIC ENGINEERING AND REPRODUCTIVE MEDICINE: EMBRYO PHENOTYPE PREDICTION, CLINICAL APPLICATIONS AND ETHICAL CHALLENGES

Abstract

Artificial intelligence (AI) is rapidly transforming the fields of genetic engineering and reproductive medicine by enhancing data analysis, predictive modeling, and clinical decision-making. In assisted reproductive technologies (ART), AI-based algorithms are increasingly used for embryo assessment, phenotype prediction, genetic screening, and optimization of in vitro fertilization (IVF) outcomes. Machine learning and deep learning techniques enable the analysis of large genomic datasets, facilitating the identification of genetic abnormalities and improving embryo selection processes. AI also contributes to personalized reproductive healthcare by integrating genomic, phenotypic, and clinical information. Despite these advancements, significant ethical concerns remain, including data privacy, algorithmic bias, reproductive inequality, and the possibility of non-therapeutic genetic enhancement. The prediction of embryo phenotypes raises questions regarding human autonomy, social justice, and the moral boundaries of reproductive technologies. This article examines the current applications of AI in genetic engineering and reproductive medicine, evaluates its effectiveness in embryo phenotype prediction, and discusses the ethical and regulatory challenges associated with its implementation.