By 2026, the global FemTech market will reach $75 billion with three major shifts emerging: AI-powered diagnostics that predict health outcomes before symptoms appear, clinical-grade wearables with medical device accuracy, and unified data platforms that synthesize information across apps, devices, and health records. 

These technologies are reshaping how women monitor fertility, manage menopause, and track pregnancy, with personalized insights becoming a core expectation in FemTech apps. 

How is AI transforming women's health predictions?

AI is moving FemTech from basic tracking to active health guidance and coaching by using contextual data. Machine learning models trained on wearables data can now accurately predict fertility windows based on menstrual cycle patterns, body temperature fluctuations, and other symptoms.

Natural Cycles, the world’s first birth control app, uses AI algorithms that identify ovulation through basal body temperature analysis. The app processes over 20 million temperature readings daily, achieving 98% effectiveness with perfect use. Flo Health uses AI to analyze over 70 symptoms and events, while the Clue algorithm is built to estimate the next period, the fertile window, and PMS, helping users predict physiological processes throughout the cycle.

Beyond fertility tracking, predictive analytics have the potential for pregnancy monitoring. Several key physiological metrics change throughout the pregnancy, with heart rate (HR), heart rate variability (HRV), and body temperature being the most prominent, all of which are tracked by most wearable devices. A 2024 study using an Oura ring to track 120 pregnancies found that continuous monitoring throughout pregnancy can detect physiological changes associated with early pregnancy loss and track distinct patterns across different trimesters. The study determined that body temperature, HR, and HRV have characteristic trajectories that could identify complications. Thus, wearables or medical devices connected to AI could supplement traditional prenatal care by providing continuous monitoring between clinical appointments, potentially enabling earlier detection of pregnancy complications.

The importance of clinical-grade wearables in women’s health

As AI-driven insights gain traction, the gap between consumer wellness devices and medical-grade diagnostics is closing. Women expect their wearables to provide actionable clinical insights about their cycle, hormone health, and pregnancy.

Oura Ring generation 4 includes temperature sensors accurate to 0.13°C, achieving over 99% lab accuracy and enabling precise cycle tracking. According to a study done by the brand, the device's ovulation detection algorithm identifies ovulation in 96.4% of cycles with an average error of 1.26 days. 

Continuous glucose monitors (CGMs) are being looked at as fertility optimization tools. A 2024 review study found that insulin resistance may increase the risk of female infertility and reduce the success rates of assisted reproductive technology. Insulin resistance affects women with PCOS and non-PCOS infertile women, particularly those with irregular menstrual cycles and obesity. Furthermore, CGM devices can be of critical use in pregnant women with type 1 diabetes. 

The role of personalized health insights in FemTech

Taken together, these advances highlight a broader shift: data is becoming the foundation of women’s health. Most women are wearing some tracker to monitor their activity, sleep, and biometric data, are tracking their cycle on apps like Clue or Flo, have electronic health records from doctor's appointments, and often keep random notes of their symptoms to bring to the next one. The future of FemTech lies in connecting this data.

Maven Clinic aggregates data from wearables, lab results, and provider notes to establish a unified health timeline and connect women with healthcare specialists. The goal of their unified approach is to better coordinate and reduce unnecessary procedures.

Modern Fertility combines at-home hormone testing with historical cycle data and genetic screening to create personalized fertility timelines. Women can download their results to share with endocrinologists, eliminating information gaps that previously delayed treatment decisions.

Even more is possible by connecting raw health data directly to large language models via the Model Context Protocol (MCP). An MCP-enabled FemTech app could function as an always-available health coach, analyzing sleep patterns from an Oura Ring, nutrition logs from a food tracking app, cycle data from menstrual trackers, and lab results to provide truly personalized insights. Instead of generic advice, your app could have a contextual AI chatbot that determines that the user’s luteal phase shortened this month due to stress levels and sleep quality, suggests specific dietary adjustments if iron levels drop, or flags concerning patterns that warrant a doctor visit. By giving AI agents secure, standardized access to users’ complete health data ecosystem, MCP transforms FemTech platforms from passive trackers into intelligent coaches that understand your users’ unique physiology and health journey.

Build an outstanding FemTech app

The challenge is not a lack of ideas; it lies in connecting all the data sources. Spike Wearables API helps you connect 500+ wearables and IoT devices, integrate nutrition information via Nutrition AI, process Lab Reports, and add an MCP layer for AI-powered insights, all while meeting HIPAA and GDPR standards.

If you are interested in adding these plug-and-play features to your women's health app, you can schedule a call to discuss your requirements in more depth and explore integration options.