Why the Data Behind Women’s Health Matters
When the documentary The (M) Factor 2: Before the Pause premieres on PBS, viewers will hear from a range of experts, advocates, and innovators working to change the conversation around perimenopause. Among them is Susan Sly, founder of The Pause Technologies, a technology platform focused on supporting women’s brain and body health during midlife.
In the film, Sly speaks about an issue that sits at the intersection of technology, medicine, and ethics: the lack of reliable data on women’s health—and why that gap makes building responsible artificial intelligence models extremely difficult.
A Technology Founder with a Background in AI
Susan Sly is an entrepreneur, investor, and technology leader with deep experience in artificial intelligence and computer vision. She previously co-founded a computer vision company focused on retail optimization, bringing more than six years of hands-on experience in applied computer vision and AI systems.
Her work sits at the intersection of technology and human outcomes—an area where the quality and integrity of data are critical. That perspective informs her leadership at The Pause, a digital platform designed to provide women with education, tools, and support during perimenopause and menopause.
In The (M) Factor 2, Sly highlights an uncomfortable truth: technology cannot solve problems when the underlying data is incomplete or biased.
The Women’s Health Data Gap
Modern medicine relies heavily on clinical research and population data to guide treatments, drug development, and healthcare recommendations. But historically, women have been dramatically underrepresented in that research.
For decades, women—especially those of childbearing age—were routinely excluded from clinical trials. (Nature)
In fact:
- Until the early 1990s, women were rarely included in many clinical trials in the United States. (AAMC)
- A 1977 FDA policy effectively barred women of childbearing potential from early-phase clinical trials, a restriction that shaped research for years afterward. (Labiotech.eu)
- Even today, fewer than 30% of participants in early-stage industry-sponsored trials are women, according to analyses of clinical research participation. (Nature)
This history created a fundamental knowledge gap in medicine.
Researchers now acknowledge that male bodies were frequently treated as the default model for human biology, with findings generalized to women despite significant physiological differences. (Wikipedia)
The consequences are profound.
Because many medications and treatments were tested primarily on men:
- Doctors may lack data on how drugs affect women differently.
- Symptoms in women are more likely to be misdiagnosed.
- Treatment outcomes may vary significantly between sexes.
For example, studies show that heart attacks in women are misdiagnosed at far higher rates, contributing to significantly worse outcomes. (Alcimed)
Why This Matters for Artificial Intelligence
Artificial intelligence systems are only as good as the data used to train them.
Machine learning models identify patterns in large datasets and use those patterns to make predictions, recommendations, or classifications. But when datasets are incomplete—or historically biased—the models trained on them will inevitably replicate those biases.
That is the ethical challenge Sly discusses in The (M) Factor 2.
If AI systems are trained primarily on medical data derived from male populations, they may struggle to accurately understand or predict outcomes for women.
This is particularly concerning in emerging fields such as:
- AI-driven health diagnostics
- predictive medicine
- digital therapeutics
- menopause and perimenopause health technologies
Without representative data, even well-designed AI systems may unintentionally reinforce existing healthcare disparities.
The Opportunity to Build Better Systems
The women’s health data gap is not just a problem—it is also a massive opportunity for innovation.
Recent analyses estimate that women’s health represents a trillion-dollar global opportunity, yet it receives only a small fraction of healthcare investment. (ITIJ)
As awareness grows around perimenopause, menopause, and women’s midlife health, researchers, clinicians, and technology companies have an opportunity to rethink how data is collected and used.
For AI developers and digital health innovators, that means:
- prioritizing inclusive datasets
- designing research that reflects women’s biology and life stages
- building ethical frameworks for health AI
Changing the Conversation
Through her appearance in The (M) Factor 2: Before the Pause, Susan Sly adds a technology perspective to the broader movement advocating for women’s health.
Her message is simple but powerful:
We cannot build accurate health technology if half the population has historically been left out of the data.
Closing that gap is not just about fairness—it is essential for building the next generation of medical innovation.
And as awareness grows, initiatives like The Pause aim to ensure that women have access to the knowledge, tools, and support they need during one of the most significant transitions in their health journey.
How to Watch The M Factor 2: Before the Pause
Click here to watch the official trailer and to check your local listings for broadcast schedules.– The (M) Factor 2: Before the Pause