According to Forbes, artificial intelligence is not solving the discovery problem in women’s health—it’s making it worse. The issue isn’t a lack of information but a systematic digital erasure where platforms like Meta and Google, driven by automated brand-safety filters, classify clinical terms around menstruation, menopause, and postpartum recovery as “adult” or “sensitive” content. This leads to demonetization, downranking, and suppressed visibility for evidence-based resources. As AI models train on this already-sanitized web, they inherit and amplify these blind spots, treating the marginalized knowledge as non-authoritative. This cycle is actively shaping how patients search for symptoms and how clinicians get decision support, embedding historical disparities into the future of care with a false sense of algorithmic certainty.
The Invisible Gatekeeper
Here’s the thing that most people don’t realize: the problem starts long before an AI model is even trained. It starts with advertising and content moderation systems. Groups like the Center for Intimacy Justice have shown that these “brand safety” rules create a stark double standard. Ads for erectile dysfunction? Usually fine. Ads for menopause or vaginal dryness? Often blocked or restricted as “sexual” content. So, from the very beginning, crucial health information is deemed commercially risky. When platforms use AI to proactively downrank this “sensitive” material, the content effectively disappears—fewer links, less traffic, no revenue. It’s digital suffocation. And AI systems, which learn by looking at what’s most visible and linked to, never get a proper chance to learn from it.
How AI Confuses Popularity For Truth
This is where it gets technically scary. AI, especially the large language models and search engines we use, doesn’t evaluate truth. It evaluates signals of authority. And the biggest signal? Visibility. A site that ranks high on Google gets more backlinks and more engagement, which tells the AI, “This is important and credible.” But if women’s health content is suppressed from the start, it never earns those signals. So when an AI retrieves information, it pulls from the usual “authorities”—often large medical institutions whose foundational research has historically centered on male physiology. The result is a vicious, self-reinforcing loop. Suppressed content lacks authority, so AI ignores it, and its outputs then reflect that gap as if it were objective fact. A study in Nature Digital Medicine on diagnostic disparities is just one example of how these skewed outputs cement real-world inequities.
The Compounding Threat Of Model Collapse
And there’s a newer, more insidious threat on the horizon: model collapse. Basically, as AI models are increasingly trained on outputs from other AI models, the information at the fringes gets lost. Nuance evaporates. So what disappears first? The topics already considered “niche” or under-indexed. Think about it: women make up the majority of healthcare users, yet entire categories like endometriosis, pelvic pain, or perimenopause are treated as specialty interests. As models train on more AI-generated, sanitized data, the little detail that existed for these conditions fades faster. The model inherits the old, biased classification that menopause is a “lifestyle” issue while heart disease (with male symptom profiles) is a critical medical one. It doesn’t question the frame. It just makes the frame harder to break.
A Different Approach To AI
So, what’s the way out? Some women-led health tech companies are abandoning the traditional “prediction” model of AI. They know the data is fundamentally broken and incomplete. Instead, as noted in the reporting, they’re building “navigation” AI. These systems don’t try to diagnose you. They can’t. The authority signals aren’t there. Instead, they ask, “What happens next?” They help users interpret confusing symptoms, prepare for a doctor’s appointment they had to fight to get, and navigate a fragmented care pathway. It’s a structural workaround for a structural problem. But it’s a patch, not a fix. The core issue, as researchers like Timnit Gebru warned in papers like “On the Dangers of Stochastic Parrots,” is that AI reflects social power structures. Until we confront the visibility layer—who decides what knowledge is safe, credible, and profitable—AI will just keep handing us the same old biases, but with a terrifying, unearned confidence.
