AI in Fertility Care: Promise, Evidence and Clinical Reality

The world is entering a new demographic era. The global fertility rate has fallen from around 4.9 children per woman in the 1950s to 2.3 today, and the UN projects that it will continue to decline throughout this century. In the UK, the birth rate is even lower – 1.39 children per woman in England and Wales in 2025. As declining birth rates, delayed parenthood, and rising infertility reshape reproductive healthcare, fertility has become one of the fastest-growing areas of HealthTech investment. Investors are backing technologies that promise to make it easier to conceive, better understand reproductive health, and navigate treatment more efficiently.The global market for assisted reproductive technology is expected to reach $33.4 billion in 2026. The FemTech market, which includes all the technologies used to support women’s health, was worth an estimated $45.6 billion globally in 2025 and is projected to more than triple by 2033. 

Artificial intelligence has naturally become part of this wave. Today, AI powers cycle-tracking apps, wearable devices, at-home fertility tests and in vitro fertilisation (IVF) technologies, promising increasingly personalised insights into reproductive health. As these tools become more sophisticated, however, the conversation is shifting from what AI can do to how much trust it should earn. That trust depends not only on the quality of the recommendations it produces, but also on how transparent companies are about the data behind them: which datasets are used to train these systems, how representative and reliable they are, and where their limitations lie for both clinicians and patients. 

Digital Fertility Tools Are Changing Patient Behaviour Faster Than Clinical Evidence Is Catching Up

The evidence for fertility HealthTech is more nuanced than either its strongest advocates or harshest critics often suggest. A recent review by UCL researchers concludes that digital tools – from fertility apps and wearables to at-home tests and telehealth – can improve fertility literacy, support self-monitoring, expand access to care, and strengthen communication between patients and clinicians. As patients become more involved in understanding and managing their own reproductive health, AI is increasingly becoming part of that same journey. According to a 2026 KFF survey, one in three U.S. adults has already used an AI chatbot for health information or advice, with many turning to it before speaking to a healthcare provider. That suggests AI is becoming more than a search tool: it now helps people interpret health information before clinical decisions are made.

At the same time, the researchers argue that these benefits depend on accuracy, clinical validity and integration into healthcare pathways, warning that many fertility technologies still reach the market without sufficient evidence that their predictions are analytically robust, clinically meaningful or genuinely beneficial for patients. The challenge goes beyond AI making mistakes or hallucinating. Many fertility technologies reach patients before there is enough evidence that their predictions are clinically meaningful or actually improve outcomes, creating a risk that confidence in the technology outpaces the evidence behind it. 

That evidence gap becomes particularly visible when AI moves from explaining basic reproductive biology to supporting fertility decisions. Researchers who compared ChatGPT, Gemini, Copilot and Perplexity across 37 fertility questions found that the models were generally reliable on topics such as the menstrual cycle and conception, but became much less consistent when answering questions about IVF, egg freezing and other assisted reproductive technologies.

The explanation is surprisingly practical rather than purely technical. Basic fertility concepts are supported by decades of well-established evidence, while assisted reproductive technologies evolve much faster. As the authors note, even models that cite scientific papers can overemphasise rare conditions or outdated findings, giving confident answers that are not representative of the broader patient population.

As healthtech devices and AI tools increasingly shape how people interpret symptoms, understand test results and approach treatment decisions before entering the clinic, the standards for evidence, transparency and regulatory oversight become much higher.

Where AI Could Make the Biggest Difference in Fertility Care

While consumer fertility apps, wearables and at-home testing continue to evolve, AI is also transforming the clinical side of reproductive medicine. Rather than replacing specialists, the most promising research focuses on helping embryologists, fertility doctors and laboratory teams make more informed decisions. 

One of the most established areas of AI research in fertility is embryo assessment during IVF. By analysing thousands of time-lapse images of embryo development, machine learning models have shown impressive results in predicting embryo viability and remain one of the field’s most promising applications. At the same time, a landmark Nature Medicine trial demonstrated that strong performance on retrospective datasets does not automatically translate into better clinical outcomes, prompting researchers to raise the bar for validation. More recent work has shifted the focus even further, emphasising clinical evaluation in real-world settings as the next critical step before these systems can be widely adopted in practice.

Another promising direction is predicting ovarian response before IVF. Researchers are using AI to combine clinical indicators such as age, anti-Müllerian hormone (AMH) levels and antral follicle counts to estimate how a patient is likely to respond to ovarian stimulation before treatment begins. Recent studies suggest these models can improve treatment planning and help identify patients who may benefit from earlier intervention or a more personalised stimulation strategy, although broader clinical validation remains an important next step.

The maturity of these technologies varies considerably. Some AI tools are already assisting clinicians in IVF laboratories today, while others are still moving from promising university research toward clinical validation. That distinction matters because it reflects a broader shift across fertility care: the greatest near-term impact is increasingly coming from AI systems that support clinical workflows rather than replace clinical judgement.

From AI Breakthroughs to Improving Everyday Patient Care

It’s no secret that the main thing businesses expect from AI tools is cost reduction. And the fertility industry is no different – IVF can be a costly procedure. No wonder clinics and other companies are looking for ways to reduce costs and make treatment accessible to as many people as possible.

We cannot make IVF and fertility treatment in general easier because the biological side of it is complicated. What we can simplify is the patient journey and the clinical workflow. So we turned to AI to reduce the time our physicians spend preparing cases – one of the most valuable resources in fertility care.

In my opinion, the most useful tool today would be an AI system that is designed to analyse a patient’s complete clinical history, previous treatments and laboratory results, then organise that information into a structured case. For example, it can give physicians a medication dose suggestion based on blood test results. The clinical decision remains entirely with the doctor; AI simply reduces the time spent preparing for it.

At Plan Your Baby, we are now working with industry partners to build such a system. In our vision, it must be trained on anonymised data collected through years of fertility care, because in medicine the quality of an AI product depends as much on the clinical context behind the data as on the model itself. Every record is de-identified before being used for development, allowing the system to learn from real-world care while protecting patient privacy. 

It’s also important to underline that since this technology will become part of the clinical workflow, it cannot be treated like a conventional AI product. Such tools need to be built with medical device certification in mind from the outset, because in healthcare, deployment is only possible if the technology can satisfy the same standards expected of any other clinical tool.