From AI that can spot the healthiest embryos to personalized dashboards that predict fertility outcomes in advance, reproductive medicine is undergoing one of the most dramatic periods of innovation in its history, driven by large data sets, machine learning, and AI. For the 1 in 6 people worldwide who struggle with infertility, these breakthroughs carry enormous promise: more clarity at the outset, fewer failed cycles, and better chances of bringing home a baby. With so much hype around future technologies that “could” make a difference, it’s critical to follow the evidence and look at clinically validated solutions. Here are five ways these tools are reshaping fertility care.
Better Embryo Selection and Prioritization
Time lapse imagery and predictive analytics can lead to better understanding of which embryos will lead to a pregnancy. While each subsequent implanted embryo leads to a higher cumulative live birth rate, research published in Human Reproduction showed that use of an embryo selection algorithm to prioritize which embryos to transfer was shown to increase time to live birth (TTLB) by 6% on average.
Patient Engagement Throughout the Fertility Process
Of all the steps in the patient journey, the first visit is one of the most critical points, as it’s when roughly 40% of patients drop out. AI and machine learning can help doctors analyze the profile of an individual patient, as well as how to best support them so they don’t get frustrated and give up. New research, expected to be published in January 2026, will show the results of a churn model designed to understand patient behavior and should point to targeted interventions doctors and clinics can take to keep patients engaged up to the first visit and beyond.
Automated Follicular Measurement and Assessment
Automating previously manual processes across the fertility journey will enable more objective and reproducible measurements and ensure standardization that will drive more uniform care. One example of how automation is already making a difference is with measurement and assessment of ovarian follicles, the basic units of female reproductive biology. OSIS (Online System for Image Segmentation) a tool developed by Fertoolity, an IVI RMA-spin off, reduces the time to perform follicular segmentation from 4-5 minutes per ovary, depending on complexity and operator skill, to under one minute for both ovaries, saving ~80% of the time per scan. Across our global network, use of this tool has already saved over 5,000 clinical hours annually. Beyond the time savings, as research published in Human Reproduction showed, OSIS ensures consistent measurements across different professional profiles, supporting data-driven clinical decisions.
Predictive Models that Set Expectations Prior to Treatment
Every patient’s journey through the fertility process is different, but to date, it has been hard for doctors to give patients a truly personalized sense of what they can expect before they start and whether they will be successful. New research underway aims to develop predictive models for patient success chances. These will estimate live birth rates based on factors such as age, infertility duration, sperm quality, ovarian reserve, and genetic variations. These models will enable a transition from single-cycle planning for patients to multi-cycle strategies, informed by reliable data and benchmarks. After doctors build out an initial profile, patients will receive a report with their personalized success probabilities before starting treatment, which will help them understand in advance what their journey may look like. Truly personalized treatment models will also enable multi-cycle discount or refund programs, which can improve affordability and help patients navigate treatment with greater confidence. Work on building out these predictive models will start in January 2026.
Non-invasive Sperm Assessment and Selection
Intracytoplasmic sperm injection (ICSI) involves a doctor injecting sperm directly into an egg to aid conception and can be most helpful when there are male infertility issues. To be successful, doctors must select the individual sperm to use, introducing a level of individual judgement and unpredictability into the process. Research presented earlier this year at ESHRE, Europe’s leading event in reproductive medicine and embryology, showed that hyperspectral imaging is a compelling, non-invasive tool for sperm assessment, and when paired with ML predictive models, can help predict embryo development potential. In the future, this approach could aid sperm selection during ICSI and associate each individual sperm to a specific success rate,opening the door to a new era in sperm selection.
The Future is More Data Driven, Automated, and Objective
The field of reproductive medicine continues to better leverage the massive amount of clinic and patient data to create better tools and processes that will drive meaningful improvements for patients and outcomes. While each individual AI tool may tackle a single step or process, taken together they can be a gamechanger in overall efficiency, time to live birth, and success. Better automated processes will also ensure that whether they are at a major research center or a newly opened clinic in a previously underserved market, patients around the world can benefit from data-driven clinical decisions and high-quality care.
Juan García-Velasco, MD, PhD
Juan García-Velasco, MD, PhD, is the Chief Scientific Officer for IVIRMA Global and Director of IVI Madrid, where he attends his patients. He is also Professor of Obstetrics and Gynaecology at Rey Juan Carlos University, Madrid, Spain, where he is Director of their Master’s Degree Programme in Human Reproduction.
Professor García-Velasco graduated from University Medical School, Madrid, in 1990 and received his obstetrics and gynaecology certification from La Paz Hospital, Madrid, in 1995. He completed his PhD in Medicine at Autonoma University, Madrid, in 1995, and from 1997 to 1998 studied at Yale University, New Haven, CT, under a Reproductive Endocrinology and Infertility Fellowship.
Professor García-Velasco’s main research interests have been in IVF and endometriosis. He is the Principal Investigator of projects funded by the Ministry of Education and Ministry of Health in Spain, and has received awards from the Spanish Fertility Society, Spanish Society of Obstetrics and Gynaecology, and the European Society of Human Reproduction and Embryology. He has published over 284 peer-reviewed articles and 31 book chapters on human reproduction, endometriosis and hypo- and hyper-ovarian stimulation response. He is the Co-Editor of Reproductive Biomedicine Online.
