AI in Reproductive Biology: Transforming Fertility Assessment, ART, and Research
Shilpa Doultani *
Department of Zoology, Biomedical Technology, Human Genetics, and Wildlife Biology and Conservation, University School of Sciences, Gujarat University, Ahmedabad-380 009, Gujarat, India and National Dairy Development Board, Anand – 388001, Gujarat, India.
Prachi Sharma
Department of Veterinary Gynaecology and Obstetrics, College of Veterinary Science and Animal Husbandry, Kamdhenu University, Anand- 388001, Gujarat, India.
Prateek Makwana
Department of ART, Vasundhara Hospital Ltd., Jodhpur- 342008, Rajasthan, India.
S.P Patil
National Dairy Development Board, Anand – 388001, Gujarat, India.
S.S Layek
National Dairy Development Board, Anand – 388001, Gujarat, India.
L.B. George
Department of Zoology, Biomedical Technology, Human Genetics, and Wildlife Biology and Conservation, University School of Sciences, Gujarat University, Ahmedabad-380 009, Gujarat, India.
H.N. Highland
Department of Zoology, Biomedical Technology, Human Genetics, and Wildlife Biology and Conservation, University School of Sciences, Gujarat University, Ahmedabad-380 009, Gujarat, India.
K.K. Hadiya
Department of Veterinary Gynaecology and Obstetrics, College of Veterinary Science and Animal Husbandry, Kamdhenu University, Anand- 388001, Gujarat, India.
*Author to whom correspondence should be addressed.
Abstract
Artificial Intelligence (AI) is revolutionizing reproductive biology, transforming fertility assessment, assisted reproductive technologies (ART), and research practices. This review explores AI's impact, highlighting its potential to enhance personalized care and advance scientific understanding. In fertility assessment, AI algorithms analyze vast datasets to predict treatment success, enabling clinicians to tailor personalized treatment plans. In ART, AI improves embryo selection during in vitro fertilization (IVF) by providing objective, data-driven criteria, reducing variability, and increasing success rates.AI also optimizes laboratory workflows, automating tasks such as data analysis and interpretation, enhancing efficiency, and minimizing human error. In research, AI accelerates data analysis, facilitates knowledge discovery, and enables predictive modeling, driving innovation in reproductive biology. However, AI's integration raises ethical concerns, including patient autonomy, informed consent, and data security. Collaborative efforts among stakeholders are essential to ensure responsible AI use, balancing innovation with ethical considerations. This review examines AI's transformative potential in reproductive biology, technological advancements, and the ethical landscape, envisioning a future where AI positively impacts reproductive health and clinical practice.
Keywords: Artificial intelligence, reproductive biology, ART, reproductive health, personalized medicine