The diagnostic capability available to women is expanding faster than at any point in the history of this industry. This week alone, Xella Health launched with an AI-powered platform screening for 130+ conditions specific to female biology. Ovum raised $4 million to build a longitudinal women's health dataset from multi-omic data. Midjourney announced a medical division planning 50,000 full-body ultrasound scanners by 2031. Add the continuous hormone monitoring wave we discussed last week, wearables like WHOOP or Oura increasingly offering female-specific biomarker tracking, and the growing roster of at-home testing platforms, and you're looking at a genuine leap in what's technically possible.
But here's the question I keep coming back to: Can any of this succeed if we don't fundamentally put more value on preventive care? Better diagnostics can detect disease earlier. But if the healthcare system - and particularly the payer system - still treats prevention as a cost rather than an investment, what happens to all that early detection?
The Need Is Real
There is no doubt that better diagnostics for women are overdue. A 2019 study published in Nature Communications that I keep coming back to found that women are diagnosed an average of 4 years later than men for the same conditions. Not because they present later, but because the diagnostic frameworks, clinical training, and referral pathways were built around male presentation of disease. Endometriosis takes an average of 7-10 years to diagnose. PMOS - now understood as a multisystem metabolic condition - has a 70% undiagnosed rate according to the WHO. Cardiovascular disease in women is routinely missed because the symptoms don't match the textbook description written for men.
These are not marginal gaps. They are systemic failures in how medicine identifies and responds to disease in women. So the impulse to build better diagnostic tools - ones that screen earlier, test more comprehensively, and track changes over time - is well-founded.
What's Being Built
The current generation of diagnostic innovation is ambitious in scope. Xella Health pairs multi-omic testing - genomics, proteins, hormones - with AI to generate probability risk profiles across 130+ conditions, delivered alongside telehealth physician review. Ovum is building a longitudinal women's health dataset, using AI to make sense of biomarker data across time rather than treating each test as a standalone snapshot. Midjourney Medical, if it delivers on its claims, could make full-body imaging accessible at a scale that would transform screening for breast cancer, cardiovascular risk, bone density changes, and reproductive health conditions.
These sit alongside the at-home hormone monitoring companies we've been covering - Eli Health, Level Zero, Clair and many more - which are generating increasingly granular data about hormonal patterns across the lifecycle.
The common thread is a shift from reactive diagnostics (you have symptoms, you see a doctor, you get tested) to proactive, consumer-initiated screening.
The Care Pathway Problem
But diagnostic capability and clinical infrastructure are not the same thing. If a multi-omic platform flags early PMOS risk or elevated Hashimoto's antibodies - what happens next? Does the result integrate into the woman's electronic health record? Does her primary care provider know what to do with an AI-generated risk profile? Is there a defined clinical pathway between a consumer diagnostic result and actual treatment?
In many cases, the answer is unfortunatly no. Most consumer diagnostics operate outside the traditional healthcare system. Results arrive in an app, not in a medical chart. The woman is often left to interpret them, bring them to a clinician who may not be familiar with the platform, and advocate for follow-up care. For conditions like endometriosis or PMOS, where diagnostic delays are already measured in years, adding more data without a clear pathway to act on it risks creating a new kind of frustration: Knowing something is wrong, but not being able to get the system to respond.
Some companies are attempting to address this by building care navigation into the product - including telehealth physician review and clinical action plans as part of the membership. That's a meaningful design choice. But it's the exception. Most consumer diagnostic companies generate results and leave the care navigation to the patient.
The Reimbursement Wall
Then there's the all so important question of who pays. Annual memberships for many of these platforms run $500 or more. And even where the clinical evidence is strong, reimbursement often hasn't followed. Most multi-omic panels, multi-cancer blood tests, and AI-driven screening tools are not covered by Medicare or most commercial health insurance plans. They're sometimes HSA/FSA eligible, which helps, but doesn't change the fundamental dynamic: The women who would benefit most from early detection are often the ones least able to afford it
This structural challenge is well-documented. As a recent World Economic Forum analysis noted, "Policy and reimbursement systems have been slow to recognize the value of early detection, often prioritizing treatment over prevention."
In a way this is partly a time horizon problem. Managed care plans whose members stay enrolled for just a few years are reluctant to cover screening with high upfront costs, because the savings from prevention accrue over a longer period - often to a different insurer. And for newer multi-omic platforms and AI-driven risk models, the evidence base linking screening to cost savings is still being built. Payers want proof that early detection translates to lower total cost of care. That proof takes years of longitudinal data to generate.
There is some progress though, specifically in women's health. Starting this year, breast cancer follow-up imaging must be covered at $0 copay - previously, 35-40% of women abandoned recommended follow-up because of cost. Self-collected HPV testing will become a covered preventive service in 2027. But wins like these are incremental. Condition by condition, each requiring years of advocacy. They do not yet extend to the kind of comprehensive, multi-condition screening that the current generation of diagnostic companies is building.
The Tension
None of this means the diagnostic innovation shouldn't be built. It absolutely should. The 4-year diagnostic delay for women is real. The undiagnosed rates for conditions like PMOS and endometriosis are unacceptable. The potential for continuous monitoring, multi-omic screening, and AI-driven risk prediction to change how women's health conditions are detected is genuine.
But the diagnostic wave will only deliver on its promise if the system around it changes too. As we noted in our recent analysis of the Cleveland Clinic State of Women's Health report, 45% of women say their biggest health concern is not disease - it's whether they can afford care at all. Building better diagnostics while the affordability and care navigation problems remain unsolved risks widening the gap between the women who are screened and those who are not.
The question this entire wave depends on isn't whether the science works. It's whether we're willing to treat prevention as something worth paying for. And the answer to that usually doesn't sit with the companies building diagnostic tools. It sits with the payers, health systems, and policymakers who decide what preventive care is worth. Until that changes, we'll keep building better ways to detect disease early - and watching too many women fall through the gap between detection and care.
