General AI Doesn't Understand Women's Bodies. We Built FoXX Because We Do.
A woman opens an AI app after her six-week postpartum checkup ends, and her clinical relationship closes with it. No follow-up for pelvic floor recovery. No check-in for a mood shift that could be the first sign of postpartum depression. The app she's using was trained on data that was never built with her in mind, tested against benchmarks that don't reflect her biology, and answers to no one.
She is right to be skeptical.
Researchers who tested 13 leading AI models on women's health questions found they got it wrong roughly 60% of the time, and the failures were worst when the stakes were highest. On menopause and hormone therapy questions, even the best-performing general model answered correctly barely half the time. One model got less than a third right. Ask the same tools a clinician-level question and accuracy drops further still.
This isn't a coincidence and it isn't a technology gap that will close on its own. It's structural. Clinical trials have skewed male for decades. Women made up under 42% of participants in cardiovascular trials, the leading cause of death for women, despite being 49% of patients with the disease. In psychiatry trials, women were 42% of participants despite being 60% of patients. General AI didn't inherit a blind spot. It inherited the blind spot medicine has had for fifty years, and it repeats it at scale.
Women already know this. That's the part the industry keeps missing.
Trust Isn't Marketing. It's the Dataset.
The women's health companies that are actually producing better AI outcomes didn't start by building AI. They started by earning trust, one honest exchange at a time. Natural Cycles built its algorithm on the same rigor its founder used discovering the Higgs boson, and it worked because users came to understand their consistent data was what made the predictions sharper. Aavia's founders traced a woman's PMDD symptom pattern across her entire cycle, not just the luteal phase doctors were trained to watch, and caught it in under 100 days against a healthcare system that averages 12 years to diagnose. They caught it because she trusted the platform enough to log consistently, month after month.
That pattern holds everywhere it's been studied. A nationally representative survey found women are far more willing to share sensitive fertility and sexual health data with their own healthcare providers than with a health tech company or a public database. Trust isn't a nice-to-have layer on top of the product. It is the mechanism that produces the dataset. No trust, no data. No data, no accurate AI. General AI companies skipped the first two steps and are now surprised the third one doesn't work.
What This Has to Do With FoXX
This is exactly the bet we made when we built FoXX Health.
FoXX wasn't built as a chatbot wrapped around a general model and pointed at "women's health" as a market category. It was built as full-body symptom tracking, licensed with data from the NIH, NLM, and SNOMED, designed to turn what a woman logs into something a doctor can actually use. The Den, our community feature, exists because the women's health companies that are working didn't get there through better algorithms alone. They got there because women showed up, logged consistently, and trusted the platform enough to tell it the truth. That's not a nice story we tell about FoXX. It's the same data compact that's powering every company doing this right.
Here's the part I'll say plainly, because someone should: FoXX has been passed on by investors and accelerators who wanted the version of this pitch that leads with a general AI model and a slide about scale, not the version that leads with clinical licensing, doctor-ready outputs, and a community built on trust that compounds over months, not a demo built to impress in ten minutes. The market has decided AI matters in women's health, AI-enabled companies in the space command close to triple the valuation of non-AI peers. But that premium assumes the AI actually works. The rejections FoXX has collected were, more often than not, a bet that a fast, flashy, general-purpose AI story would outcompete a slower, harder, clinically grounded one. The data says otherwise. Every company in this space that is actually producing results, Natural Cycles, Evvy, Clue, Ema EQ, Aavia, built trust first and let the AI follow. None of them got there by skipping the hard part.
FoXX didn't skip it either. That's the whole point.
The Standard Is Already Being Set
In May 2026, women's health AI companies formed their own consortium to require accuracy benchmarks, disclosed data sources, and real clinical oversight, because they understood something the broader AI industry still hasn't caught up to: the advantage was never just technical. It's ethical. It's earned. And it shows up in the data one honest log, one honest symptom, one honest conversation at a time.
Women don't distrust AI because they don't understand it. They distrust it because they've been tested on it and it failed them when it mattered most. The companies closing that gap aren't the ones with the biggest model. They're the ones women actually trust enough to tell the truth to.
That's the company we're building. That's what FoXX is for.
Sources include reporting from Geri Stengel, "Women Don't Trust AI. They Trust Women's Health Companies," Women's Health And Market Visibility, July 9, 2026 (geristengel.substack.com), and the studies and executive interviews cited within it.
