Artificial Intelligence has now transitioned from an experimental phase to an implementation phase in most of the thinkable domains. AI and medicine is one of the most sought-after partnerships in recent times. Almost every aspect in medical science now is including AI-based advancements. Three major categories of AI methods widely used in medical applications are machine learning (ML), natural language processing (NLP), and robotic surgery.

In line with that, now AI is showing promising results in the field of reproductive medicine. It is proving meaningfully applicable in the process of IVF (In-Vitro Fertility) or ART (Assisted Reproductive Technology).

WHO has recognized infertility as the third most common issue after Cancer and Heart Disease. 

According to WHO data, "infertility affects millions of people of reproductive age worldwide – and has an impact on their families and communities. Estimates suggest that between 48 million couples and 186 million individuals live with infertility globally." This data suggests that there is a high demand for solutions such as in-vitro fertilization.

According to the Indian Society of Assisted Reproduction (ISAR), "in India, one in six couples are battling with infertility. This has resulted in millions of couples taking up various forms of fertility treatment, especially IVF." Unfortunately, not all IVF treatments produce the desired results. 

Low success rates of 35-40% lead to multiple IVF cycles per patient. This increases costs as well as stress and anxiety amongst the patients. 

In the process of IVF, the embryo selection is a critical task, as this embryo selection means picking up the embryo that is most likely to succeed. Embryos were considered good quality if the chances were greater than 58% and poor quality if the chances were below 35%. This is where doctors are seeking the help of AI. AI has capabilities to detect the embryo that is more likely to grow without any problem. This AI-powered system analyses several images and selects embryos of comparable quality to those selected by a human specialist.

Deep learning for medical imaging is an AI technique that enables computers to recognize and classify images. The algorithm trains on large datasets in order to self-learn complex patterns and features relating to image type. Once trained, the algorithm can recognize and classify new, unseen images.

Traditionally embryo assessment is done by a skilled embryologist, who examines each embryo under a microscope and assigns it a grade. This assessment has limitations such as human error or other variables that might hamper the results' precision. 

The role of artificial intelligence in Reproductive Medicine includes electronic medical records (EMRs) and other data. EMRs can capture data in various ways, and the data is analyzed using AI, such as machine learning and natural language processing (NLP). AI has been used in many aspects of reproduction, from research and experiment to clinical practice. 

GoHealthe is a company that offers turnkey solutions in assisted reproduction and has partnered with Life Whisperer, the fertility arm of AI healthcare company Presagen, which operates out of Adelaide (South Australia) to provide AI-powered embryo selection in India.

Talking about challenges, data collection in a precise and accurate way is a challenge that needs to be dealt with sooner. The practices need to change in order to improve outcomes. We need to also take care of eliminating any biases in these algorithms and move beyond just embryo selection and aim to make an impact in treating stubborn infertility cases too. This can also help in early detection of the root cause of infertility.

The future of ART or infertility treatments is AI for sure. With algorithms getting more refined and the availably of data improving, we can expect breakthroughs in current AI applications in the direction of medical development. With AI intervention, the IVF process can become more transparent and help people make informed choices.