Women’s healthcare has long been underserved when it comes to technological innovation. While advancements in digital health have transformed diagnostics, disease management, and personalized medicine, fertility tracking remains surprisingly outdated. Millions of women still rely on decades-old methods—handwritten cycle charts, manual interpretation of test strips, and one-size-fits-all predictions—despite the availability of artificial intelligence and data-driven solutions that could dramatically improve accuracy and accessibility.
Biotechnology entrepreneur Janet Zhang is determined to change that. As co-founder of Boston Easy Biotech and a Forbes Business Council member, Zhang has spent years at the forefront of medical diagnostics, specializing in advanced diagnostics and cutting-edge biochemistry. Her experience in biotech investments and recurring role as a featured Forbes Expert Panel contributor on women’s health technology have given her unique insights into a sector that, despite its enormous potential, remains underfunded and technologically stagnant. Now, she’s applying that expertise to a problem millions of women face: unreliable fertility tracking.
With the development of an AI-powered ovulation detection platform, Zhang is rethinking how women monitor their reproductive health. By leveraging artificial intelligence to automate test interpretation and analyze hormonal trends, her system aims to replace guesswork with precision—bringing fertility tracking into the era of personalized, data-driven healthcare.
The Problem with Traditional Fertility Tracking
For decades, ovulation tracking has relied on luteinizing hormone (LH) test strips, which detect hormone surges to estimate fertility windows. While inexpensive and widely available, these strips require manual interpretation: users must compare line darkness, judge hormone levels, and log their results by hand. This process is not only tedious but also prone to errors that can lead to mistimed conception attempts.
Beyond the challenge of subjective interpretation, traditional LH tests offer only a single, static data point—providing a momentary glimpse into hormonal changes rather than a comprehensive understanding of a woman’s cycle. Without a system that tracks trends over time, many women struggle to pinpoint their most fertile days with confidence.
Accessibility is another major barrier. While advanced fertility monitoring exists, it often requires expensive medical-grade devices or frequent in-person consultations, making it impractical for many. Despite the growing role of digital health in diagnostics, fertility tracking has remained largely untouched by AI and machine learning—something Zhang believes must change.
Bringing AI to Fertility Tracking
Advancements in AI and machine learning are transforming nearly every aspect of healthcare, and reproductive health is no exception. Zhang’s fertility app represents a significant leap forward, offering a system that learns from historical hormone patterns to build a predictive model tailored to each user. Rather than relying on a single test result, the AI analyzes historical hormone trends, creating a dynamic, data-driven forecast of a woman’s cycle.
One of the most immediate benefits of Zhang’s approach is automated test interpretation. Users simply take a photo of their LH test strip, and the AI precisely determines hormone levels, avoiding the uncertainty of manual readings. The system then logs the data, adjusts predictions accordingly, and presents a clear, interactive ovulation calendar.
The companion Famwell LH test strips, optimized for use with the app, achieve over 99% detection accuracy for LH surges. This high level of precision ensures that users can trust their fertility insights—whether they’re trying to conceive, managing conditions like polycystic ovary syndrome, or simply seeking better reproductive health awareness.
Importantly, this technology empowers women with knowledge about their reproductive health. By offering a comprehensive, AI-driven view of ovulation trends, the platform provides women with greater control over their reproductive health. Those with irregular cycles or hormonal conditions, who often struggle with conventional tracking methods, now have access to a system that learns and adapts to their unique biology.
The Future of Women’s Health is Data-Driven
Despite the booming FemTech market, which now attracts 3% of all digital health funding, many fundamental aspects of reproductive care remain technologically outdated. Zhang sees AI-powered fertility tracking as part of a larger movement toward precision medicine in women’s health—one that prioritizes data accuracy, accessibility, and user empowerment.
With the Famwell LH App set to launch in the coming months, Zhang’s work is poised to offer a long-overdue alternative to outdated fertility tracking methods. By shifting reproductive health from static predictions to intelligent, continuously improving systems, she’s not only making fertility care more effective—she’s modernizing women’s healthcare for the digital age.
“We’ve seen AI transform everything from early disease detection to personalized medicine,” Zhang explains. “Fertility care should be no different. Women deserve better tools, better data, and better outcomes—and AI can provide that.”