Despite advances in medicine, the gender health gap remains one of the most entrenched inequities in global healthcare. Women spend 25% more of their lives in poor health than men, a disparity fueled by biological differences, gender‑based inequities, and a long legacy of inadequate research representation. Artificial intelligence (AI), if designed and deployed responsibly, offers a chance to correct these long‑standing failures. But for AI to uplift women’s health rather than reinforce existing biases, women must be central in leading, shaping, and governing this technological shift.

by Esraa Khatab, Assistant Professor, Mathematical and Computer Sciences, Heriot-Watt University Dubai

Why AI Is Uniquely Positioned to Transform Women’s Health

For decades, medical research prioritized male bodies, often treating women as “small men”—a systemic bias that has led to misdiagnosis, delayed treatment, and under‑recognition of conditions that disproportionately affect women. The World Economic Forum highlights that women’s health has been “systematically underserved,” resulting in widespread misdiagnosis, including the finding that women with heart attack symptoms are 50% more likely to be misdiagnosed than men.

AI offers a turning point because it can analyze vast, multimodal datasets and detect patterns that elude traditional research. JAMA researchers emphasize that AI can identify sex‑specific differences in disease presentation and treatment response, enabling more accurate, personalized care. Whether it is cardiovascular disease, autoimmune disorders, or chronic pain conditions, AI can help expose previously hidden trends and guide earlier, more targeted interventions.

A powerful example is menopause research, an area that affects nearly 85% of women yet historically received little scientific attention. According to the U.S. National Science Foundation, AI‑driven analysis of genetic, hormonal, and behavioral data is now enabling more precise prediction of menopause‑related health risks and improving symptom management. This shift demonstrates AI’s potential to give long‑neglected women’s health issues the scientific rigor they have always deserved.

The Risk: AI Can Reinforce the Very Biases It Is Meant to Fix

AI’s promise is immense, but so is the danger of embedding gender bias into its foundations. The World Economic Forum and others warn that data deserts—gaps where women’s health data is sparse or inconsistent—pose major threats to equitable AI.

AI systems on male-centric datasets risk inaccurate diagnosis, misinterpreted symptoms, or deprioritizing conditions that mainly affect women. Pharma’s Almanac similarly cautions that biased training data and male-dominated algorithm design can produce AI tools that worsen gender disparities instead of correcting them.

This is not a theoretical problem. If an AI diagnostic model is trainedon datasets where women are underrepresented, it may “learn” to overlook female-specific symptoms. If wearable technologies are calibrated on male physiology, they may misread women’s vital signs or miss early warnings. Bias in = bias out –but automated.

Why Women Leaders in AI Are Essential

The gender health gap is inseparable from the gender leadership gap in AI and digital health. When women are absent from research teams, executive boards, and design committees, their lived experiences and priorities are equally absent from the systems shaping the future of care.

Multiple analyses show that biased datasets are often accompanied by biased decision‑making structures. DigitalVital Hub emphasizes that male‑dominated development teams frequently overlook sex‑specific considerations, leading to AI design choices that disadvantage women.

Women leaders bring essential perspectives to research design, consent practices, ethics frameworks, and evaluation criteria. Their presence increases the likelihood that sex‑disaggregated data will be collected, that overlooked conditions will be prioritized, and that algorithms will be evaluated through an equity‑centered lens.

Furthermore, the economic stakes are enormous: McKinsey estimates that closing the women’s health gap could add over $1 trillion to the global economy every year by 2040. Investing in women‑led AI innovation is not only a moral imperative — it is a catalyst for global economic growth.

How We Move From Possibility to Progress

To ensure AI closes—not widens—the health gap, the industry must take decisive action:

· Require sex-disaggregated data in clinical trials, digital health tools, and AI training sets.

· Audit algorithms for gender bias before deployment in clinical settings.

· Support women-led AI startups, research labs, and healthtech initiatives.

· Mandate diverse leadership in AI development, governance, and regulatory bodies.

· Invest in women’s health research, especially in historically under-studied areas such as endometriosis, PCOS, menopause, and autoimmune disorders.

We are entering what experts describe as an “action stage,” shifting from identifying the problem to scaling solutions. The coming years will determine whether AI becomes a tool for progress — or a faster way to automate inequity

A Call to Lead the Future

AI will shape the next century of healthcare, but whether it accelerates or dismantles gender inequities depends entirely on who leads its design. Women bring the insight, urgency, and lived experience needed to ensure AI fulfills its promise for equitable care.

To close the gender health gap, we must center women — as data contributors, researchers, founders, policymakers, and visionaries.

Technology alone won’t close the gap. But technology shaped by women can.