On International Women’s Day 2026, leaders across industry, philanthropy, policy, academia, health systems and finance are uniting with a shared message: bias in women’s health research and funding is systemic and its consequences extend far beyond the women’s health sector.
Even though evidence from the World Economic Forum shows that women’s health is a $1 trillion opportunity, it captures only 6% of private healthcare investment.
For decades, what gets studied, measured, funded and scaled in health has followed patterns that did not consistently prioritize women’s biological realities, lived experiences or economic contributions.
For instance, only around 5% of clinical trials report results disaggregated by sex- without sex-disaggregated reporting, evidence is incomplete and can undermine safety and effectiveness.
While awareness of these gaps has grown, structural incentives in research, capital allocation and policy decision-making continue to shape uneven outcomes.
This collection brings together perspectives from global leaders across sectors, from pharmaceutical research and health systems to consulting, philanthropy and investment.
They examine where blind spots remain, how funding models influence priorities and what must change to ensure women’s health is no longer treated as a niche category.
Correcting bias in women’s health is not the responsibility of any single actor. It requires coordinated action across industry, governments, investors, philanthropies, employers and care providers. The voices below reflect that multistakeholder imperative.
What global leaders say about bias in women’s health research
Fiona Marshall, President of Biomedical Research, Novartis
Emerging technologies can help address bias in research.
Meaningful patient representation at every stage of research and development, from preclinical research through clinical trials to post-marketing studies, is critical for preventing and correcting bias.
Women are often not adequately represented in research and clinical trials, even for conditions that disproportionately affect them, such as autoimmune disease. This can lead to gaps in understanding and ultimately, worse outcomes.
Structurally speaking, the widespread adoption of emerging technologies such as artificial intelligence offers an immense opportunity to address research bias by leveraging data at unprecedented scale, so long as the underlying data accurately capture sex-based differences and do not reinforce existing gaps.
This is one important way to better identify and dismantle barriers contributing to bias in drug discovery and clinical trials.
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