AI in Women’s Health: The Cure We Need or Just More of the Same?

AI is shaping the future of medicine—but is it failing women?

AI promises to be the great equalizer in medicine—faster, smarter, more precise. However, AI is only as good as the data it’s built on. If that data is flawed, incomplete, or biased, then AI won’t fix healthcare, and instead, it will automate the same problems we’ve dealt with for decades.

We already know the healthcare system is broken. Women have been left out of research, dismissed in medical settings, and underrepresented in leadership. Thus, if AI is learning from this system, we have to ask: What exactly is it learning?

The answer: a deeply imperfect past.

When algorithms inherit decades of bias, women’s health pays the price.

For decades, women have been left out of research, misdiagnosed, and dismissed in medical settings.

• Heart disease is the leading cause of death in women, yet AI models trained on male-centric data fail to recognize women’s symptoms. Women are 50% more likely to be misdiagnosed after a heart attack and 50% more likely to die within a year.¹

• Machine learning for bacterial vaginosis performs worse for Asian and Hispanic women, proving AI is only as accurate as the biased data it’s fed.²

• Women weren’t included in clinical trials until 1993, and today, only one-third of studies analyze data by sex. ^3,4 If AI isn’t trained with diverse, sex-specific data, it will scale these blind spots into the future.

The issue isn’t AI itself—it’s that the foundation of data is fundamentally flawed.

If past research has overlooked women’s symptoms, then AI will, too. If diseases that disproportionately affect women—like autoimmune disorders, osteoporosis, and chronic pain—have been underfunded, AI-driven healthcare will reflect that same lack of attention.

And here’s the real danger: once an algorithm labels a woman’s symptoms as “psychosomatic” or calculates that she’s at lower risk for a disease because of outdated data, that mistake gets locked in. It stops being a judgment call and starts being an objective data-driven decision — even if it’s based on the same faulty logic medicine has used for decades.

AI won’t “fix” healthcare overnight. If we don’t change the foundation—how research is conducted, who is represented, what gets funded—then AI will continue scaling the same biases we’ve been trying to undo.

This dynamic then snowballs into healthcare reimbursement models. 

Why Reimbursement Models Are Failing Women’s Health—and How AI Fits In

Even the best technology won’t change healthcare if the financial incentives remain broken.  Let’s use hysterectomy as an example.

• Hysterectomy is one of the most frequently performed surgeries in the U.S.—despite ACOG stating it should NOT be the default treatment.⁴ Yet, insurance often makes it easier to get reimbursed for surgery than for less invasive options, which could include pelvic PT or hormone therapy.

• Even within hysterectomy procedures, reimbursement trends don’t align with best medical practice. Minimally invasive laparoscopic hysterectomy rates have dropped—not because they aren’t safer, but because reimbursement favors other approaches.⁵

💡 If AI is layered on top of this broken system, it won’t drive better care—it will optimize for what gets reimbursed, not for what’s best for patients.

A Real-World Example: The Menopause Care Gap - How AI, Reimbursement, and Research Must Align

We’ve seen what happens when bad policy and misinterpretation of research shape women’s healthcare.

• In 2002, the Women’s Health Initiative study was widely misinterpreted, leading to the fear-driven reduction of hormone therapy prescriptions.
  Doctors stopped prescribing hormone therapy—even when it was the right treatment.

The result?

• Millions of women suffered from preventable health issues like osteoporosis, brain fog, and cardiovascular disease.

• A generation of women was left untreated—not because the care wasn’t there, but because the system failed them.

Now imagine AI being built on flawed data, outdated research, and broken reimbursement policies.

If we don’t fix these issues first, AI will reinforce decades of harm—not solve it.

The Hope: How AI Could Transform Women’s Health 

This isn’t just about problems—there’s real hope if we get this right.

💡 Imagine a future where clinical trials, FDA approvals, medical guidelines, and reimbursement all align seamlessly.
💡 Where AI accelerates progress—not by scaling old biases, but by leveraging high-quality, inclusive data to drive faster and more accurate decision-making.
💡 Where conditions like endometriosis—where women now wait 7-10 years for a diagnosis—are identified in months, not a decade.⁷
💡 Where the 17-year gap between research and clinical practice collapses into months, ensuring breakthrough discoveries reach patients sooner.⁸

🚀 That’s the future AI could help create—but only if we fix the foundation first.

✔️If the data is incomplete, AI will scale those gaps.

✔️If reimbursement rewards outdated interventions, AI will optimize for the wrong things.

✔️If clinical trials still exclude women or fail to analyze data by sex, AI will reinforce medical blind spots.

The Call to Action: What Needs to Happen Now

The good news? This isn’t inevitable—we can fix it.

🔹 We must demand better data. AI models can’t be built on incomplete research. Sex-specific data and diverse clinical trials must be standard, not optional.

🔹 We must invest in women’s health as an economic and public health priority. Not just fertility and menopause, but Alzheimer’s, autoimmune disease, cardiovascular health, and beyond.  

🔹 We must ensure AI-driven healthcare aligns with reimbursement and real-world clinical practice. If doctors can’t get reimbursed for treatments AI recommends, the system still fails.

Technology is a tool, not a solution.

The future of healthcare must be built with women in mind—because if we don’t change course now, the next generation will face the same barriers, only harder to challenge.

Next Steps: Get Involved

💡 Subscribe to Fempower Health for the latest insights on women’s health, innovation, and advocacy.
💡 Share this post to spread awareness about the urgent need for change in AI and women’s healthcare.
💡 Join the conversation—what do you think needs to happen next? Comment below.

🚀 Women’s health is an opportunity—let’s ensure AI helps, not harms.

References

1. Go Red for Women. The Facts about Women and Heart Disease. 

2. Celeste, C., Ming, D., Broce, J. et al. Ethnic disparity in diagnosing asymptomatic bacterial vaginosis using machine learning. npj Digit. Med. 6, 211 (2023). https://doi.org/10.1038/s41746-023-00953-1

3. Institute of Medicine (US) Committee on Ethical and Legal Issues Relating to the Inclusion of Women in Clinical Studies; Mastroianni AC, Faden R, Federman D, editors. Women and Health Research: Ethical and Legal Issues of Including Women in Clinical Studies: Volume I. Washington (DC): National Academies Press (US); 1994. B, NIH Revitalization Act of 1993 Public Law 103-43. Available from: https://www.ncbi.nlm.nih.gov/books/NBK236531/

4. Merone, L., Tsey, K., Russell, D., & Nagle, C. (2022). Sex Inequalities in Medical Research: A Systematic Scoping Review of the Literature. Women's health reports (New Rochelle, N.Y.), 3(1), 49–59. https://doi.org/10.1089/whr.2021.0083

5. ACOG, Choosing the Route of Hysterectomy for Benign Disease, 2017 June.

6. H.R. Newman, S. Ghaith, S.S. Voleti, P.M. Magtibay, J. Yi, An Analysis of Medicare Reimbursement Rates in Hysterectomies Performed in Gynecologic Surgery: 2010-2019, Journal of Minimally Invasive Gynecology, Volume 27, Issue 7, Supplement, 2020, Page S114, ISSN 1553-4650, https://doi.org/10.1016/j.jmig.2020.08.182.

7. Johnston, J. L., Reid, H., & Hunter, D. (2015). Diagnosing endometriosis in primary care: clinical update. The British journal of general practice : the journal of the Royal College of General Practitioners, 65(631), 101–102. https://doi.org/10.3399/bjgp15X683665

8. Rubin R. It Takes an Average of 17 Years for Evidence to Change Practice—the Burgeoning Field of Implementation Science Seeks to Speed Things Up. JAMA. 2023;329(16):1333–1336. doi:10.1001/jama.2023.4387