Why AI Is a Defining Test for Women’s Health
Artificial intelligence is reshaping healthcare at remarkable speed. Yet one critical question remains largely unanswered: will it work equally well for women? According to Paula Bellostas Muguerza, Global Head of Healthcare and Life Sciences at Kearney, and Elena Bonfiglioli, Global Business Leader for Healthcare at Microsoft, AI is not just a solution for women’s health — it is a test of whether the healthcare system is finally ready to treat women fairly.
In a recent podcast conversation, both leaders argue that AI represents a pivotal inflection point. It can either replicate decades of bias, or it can become the most powerful tool yet for closing the women’s health gap. The outcome depends entirely on how the technology is designed, trained, and deployed.
The Women’s Health Gap Is Real and Costly
First, it is important to understand how deep the gap runs. Only 7% of healthcare research focuses on conditions that exclusively affect women. Furthermore, just 5% of available medications carry full safety information for women who are pregnant or breastfeeding. These are not minor oversights — they are systemic failures with serious consequences.
As a result, women live in poor health for 25% more of their lives compared to men. There is, as Muguerza explains, a meaningful difference between lifespan and health span. Women may live longer, but they live more of those years in pain, illness, or disability. Additionally, persistent bias across the health value chain continues to delay diagnoses and limit treatment options for women worldwide.
Addressing these gaps is not only a moral imperative. According to a World Economic Forum report, bridging the women’s health gap could give women seven more healthy days per year and inject one trillion dollars into global GDP within 15 years.
How AI Can Either Fix or Worsen the Problem
Here is where AI becomes both the opportunity and the risk. AI systems learn from historical data. However, that data has historically excluded or underrepresented women. Consequently, if AI tools are trained on biased datasets, they will reproduce and even amplify those same biases at scale.
Early diagnostic tools, clinical decision support systems, and drug interaction algorithms all carry this risk. For instance, an AI model trained primarily on male patient data may consistently underperform when assessing female symptoms. Moreover, conditions like endometriosis — which affects one in ten women — remain chronically underdiagnosed partly because the data trail is so thin.
Therefore, the design choices made today will determine whether AI becomes a leveller or a multiplier of inequality in women’s healthcare.
Technology as the Great Equaliser
Nevertheless, the potential for positive change is immense. Bonfiglioli describes technology as “the invisible power working in the background that enables better health outcomes.” When built inclusively, AI can do what human systems have repeatedly failed to do — analyse patterns without unconscious bias and flag conditions that clinicians might otherwise miss.
AI-powered diagnostics are already improving early detection of breast and ovarian cancers. Personalised care platforms are helping women manage endometriosis, menopause, and fertility challenges with far greater precision than before. Meanwhile, in underserved communities, AI-driven mobile health tools are removing access barriers that have historically kept women out of the healthcare system altogether.
Additionally, Sans Thakur, another contributor to these discussions, notes that “technology is the great accelerator, making edge case innovation accessible and changing the health paradigm.” Conditions once considered niche or rare — and therefore under-resourced — can now receive the research attention they deserve, thanks to AI’s ability to scale.
What Leaders and Organisations Must Do Now
Closing the gap requires deliberate action at every level. Muguerza highlights three structural changes that organisations must prioritise.
Invest in women-specific data. Clinical trials must include proportional female representation. AI models built on inclusive datasets will deliver far more equitable outcomes. Currently, this is not the norm — and it must become one.
Build incentive structures for change. Policy plays a vital role. Governments and regulators must create frameworks that reward investment in women’s health research and penalise the use of AI tools that lack gender-inclusive validation.
Educate leaders across all sectors. Women represent 70 to 80 percent of the global healthcare workforce. They also make approximately 80 percent of health and economic decisions within families. Yet they remain underserved as patients. Leaders in technology, consulting, and healthcare must understand this contradiction and act on it.
Muguerza’s [w]Health platform — now a community of 350 organisations — exemplifies what cross-sector collaboration looks like in practice. Sharing best practices and aligning on common standards can accelerate progress across the entire health value chain.
A Trillion-Dollar Opportunity Worth Seizing
Ultimately, the case for investing in AI-driven women’s health is both ethical and economic. Healthy women build healthier families, healthier workforces, and stronger economies. When women cannot access quality care, the costs ripple outward — through lost productivity, higher emergency care utilisation, and generational health disadvantage.
AI is here. The question is whether the people deploying it will have the courage and commitment to build it right. As Muguerza and Bonfiglioli make clear, women’s health is no longer a niche concern. It is the ultimate test of whether AI in healthcare will live up to its promise.
The technology exists. The economic argument is clear. Now the healthcare industry must choose to act — and act equitably.
