FertilAI secures CE mark for predictive AI tools targeting fertility treatment timing
The newly cleared platform focuses on one of the most complex and variable aspects of fertility treatment
Fertility technology company FertilAI has received CE marking under the EU Medical Device Regulation (MDR) for its AI-driven clinical decision support tools, marking what the company says is the first regulatory clearance for a predictive AI platform designed to optimise timing decisions during active fertility treatment cycles.
The approval covers FertilAI’s Fertilane platform, which integrates with electronic medical record (EMR) systems and provides clinicians with real-time, data-driven insights during IVF and other fertility treatments.
“Our goal is to accelerate and ease the patients’ fertility treatment journey to parenthood, no matter the path taken,” said Rohi Hourvitz, chief executive officer of FertilAI.
“Achieving the CE mark under the EU MDR validates our technologies’ ability to support more effective clinical decision making and simultaneously streamline clinic operations, all while increasing patient access to services without growing clinic overhead.”
AI targeting a critical decision point in fertility care
The newly cleared platform focuses on one of the most complex and variable aspects of fertility treatment: timing.
By analysing data from more than 100,000 treatment cycles, FertilAI’s algorithms are designed to predict ovulation and estimate the number of mature oocytes that can be retrieved at different trigger points - helping clinicians determine the optimal moment for intervention.
According to the company, this allows physicians to make more precise decisions during active treatment cycles, while also supporting clinic operations.
“To our knowledge, this is the world’s first CE-marked predictive AI-based fertility platform specifically designed to support the physician’s clinical timing decisions during active treatment cycles,” the company said in a statement.
Two algorithms targeting IVF and natural cycles
The CE mark covers two core tools within the Fertilane platform:
StimAI (Class IIa) predicts the expected number of mature oocytes retrieved based on different trigger timings, supporting IVF decision-making and enabling clinics to better manage laboratory workloads
OvuPredict (Class I) forecasts natural cycle ovulation up to six days in advance, which - based on published research in Nature Scientific Reports - may improve timing for intrauterine insemination (IUI) and frozen embryo transfer (FET), and reduce the need for intensive patient monitoring
Together, the tools are designed to support more personalised treatment while helping clinics manage daily scheduling and resource allocation.
Addressing access and capacity challenges
The approval comes as fertility services face rising demand globally, alongside workforce constraints and increasing treatment costs.
Rohi Hourvitz added that the platform could play a role in addressing capacity challenges in the sector.
“With the global rise in infertility and a worldwide shortage of physicians driving higher costs and longer wait times, expanding access to care is absolutely vital,” he said.
“Having a system that enables clinic scalability while actively improving clinical benefits and the patient experience is a pivotal step forward in addressing this growing challenge.”
FertilAI said the Fertilane platform is designed as a seamless SaaS solution that integrates directly into existing EMR systems, allowing clinicians to access AI-driven insights within their current workflows.
The company added that its technologies have been validated through multiple published studies in peer-reviewed journals, including Fertility and Sterility, Reproductive BioMedicine Online, and Nature Scientific Reports, with further randomised controlled trials ongoing.
The CE mark places FertilAI among a growing cohort of companies seeking to apply artificial intelligence to fertility treatment, an area characterised by high variability in outcomes and significant operational complexity.