Advancement in reproductive therapy and use of Artificial Intelligence

Artificial Intelligence (AI) offers unprecedented potential to analyze complex biological data, detect subtle patterns in imaging and clinical history, and support evidence-based decisions in embryology, endocrinology, and genetics. These advancements promise to improve efficiency, reduce trial-and-error in treatment cycles, and expand access to care, especially for patients with poor prognosis or complex fertility issues. This research collection seeks high-quality submissions that explore how AI technologies are shaping the next generation of reproductive treatments. It welcomes studies focused on both laboratory-based and clinical applications of AI, as well as ethical, regulatory, and translational challenges. Submissions from reproductive endocrinologists, embryologists, AI scientists, fertility specialists, bioethicists, and health technology developers are encouraged.

Topics of interest (not limited to):

• AI-driven embryo grading and selection algorithms in IVF

• Machine learning models for predicting ovarian response and treatment outcomes

• Use of AI in optimizing controlled ovarian stimulation (COS) protocols

• Deep learning in time-lapse imaging analysis of embryo development

• AI-assisted diagnostics in male infertility and sperm morphology assessment

• Integration of AI with genetic and epigenetic screening in pre-implantation embryos

• Development of clinical decision-support systems for personalized fertility care

• Predictive analytics for success rates, miscarriage risk, and live birth probability

• Natural language processing (NLP) for analyzing electronic health records in ART

• Ethical considerations and bias mitigation in AI models used in reproductive medicine

• Regulatory challenges and standards for validation of AI tools in clinical fertility labs

• Comparative studies of AI vs. conventional assessment in reproductive outcomes

All submitted papers will undergo rigorous peer-review by specialists in the field, with accepted papers set to be published as soon as they are ready.

This collection support United Nations Sustainable Development Goals 3: Good Health & Well-Being.