.png){kind=logo}
Genetic testing is becoming an increasingly important component of reproductive health care. It has evolved, over the years, from detecting genetically inherited disorders to being used in guiding treatment decisions, assessing risks during treatment, and evaluating pregnancy outcomes. Today, technologies based on artificial intelligence and machine learning (AI&ML) are further revolutionizing reproductive genomics by allowing faster and more accurate analysis of complex genetic data, helping healthcare professionals to make more informed clinical decisions for improved patient outcomes.
As DNA sequencing technologies have continued to evolve, much larger and more complex volumes of genomic data are being generated. AI&ML are now helping clinicians analyse and interpret this large amount of information more efficiently, especially in cases where standard techniques fall short due to the complexity of data.
One area where this is becoming increasingly visible is embryo assessment during IVF. Application of AI in Pre-implantation Genetic Testing (PGT) is supporting clinicians across multiple stages of IVF, from gamete assessment to embryo selection. It also helps interpret PGT data, by enabling analysis alongside clinical and population level data which allows for better identification of benign vs. potentially clinically significant variants, providing greater clarity when selecting embryos.
AI-based approaches are also being used to analyse single nucleotide polymorphisms (SNPs), helping improve the understanding of inherited genetic patterns in PGT. In parallel, AI-assisted interpretation of next-generation sequencing (NGS) and long-read sequencing data is improving the identification of both smaller genetic alterations and larger structural variations.
AI is also helping improve how existing reproductive genetic tests perform in everyday clinical practice. The use of machine-learning models in the analysis of variables such as fetal fraction and fragment size distribution within NIPT has improved the interpretation of genetic signals associated with chromosomal abnormalities and reduced rates of inconclusive results requiring further testing.
AI&ML-based tools are now also being developed to integrate genomic findings with patient's medical history, and other biological inputs to provide a more precise assessment of an individual's reproductive risks, success of IVF treatments, and potential complications during pregnancy. This allows clinicians to offer targeted treatment plans, reflecting a broader move toward more personalised reproductive care.
Looking ahead, several emerging applications continue to be explored, and AI&ML-based systems are expected to become increasingly refined over time.
AI can be extremely useful in analysing large amounts of genetic data, identifying patterns, and detecting precise mutations much faster than traditional approaches. However, reproductive genomics tests produce results for individuals and families with very personal considerations relative to their reproductive choices, pregnancy, and long-term health. For this reason, interpreting genetic findings still requires much more than technology alone.
Clinical experience, careful judgment, and meaningful patient counselling remain essential in helping people understand what these results could mean personally to them. In this context, AI should be considered a support mechanism for clinicians; not a replacement. It can simplify complex data, but medical judgment, empathy and thoughtful human guidance remain essential to supporting patients through reproductive decisions.
