Shrinking the gender health gap through innovation in clinical trial AI

By Paula Bellostas Muguerza, Global Lead, Healthcare and Life Sciences at Kearney

The lack of female representation in clinical research is finally getting the recognition it deserves as a driving factor of the gender health gap.

With only 7 per cent of healthcare research focused on conditions that exclusively impact women’s health, it’s an area that has frustratingly been overlooked and underfunded for decades.

Clinicians have been forced to make decisions about women’s health based on limited data and male-centric trials.

As a result, women are routinely subjected to missed diagnoses and delayed treatments.

However, like nearly every sector, AI is reshaping healthcare, and if used correctly, there’s a real opportunity to redesign clinical trials, close long-standing gaps, and make research far more inclusive and effective.

Incorporating AI

Unlocking innovation in women’s health, including more diverse women in clinical trials, disaggregating data by sex, redesigning clinical trials with women in mind, and deepening insights on sex differences were all recognised as core principles for improving healthcare policy in the World Economic Forum’s recent “Prescription for Change” white paper, in collaboration with Kearney and the Gates Foundation.

This focus is critical in improving healthcare outcomes for women.

Conditions such as heavy menstrual bleeding, endometriosis, fibroids, and ovarian cysts cost the UK economy approximately £11 billion per year, but fixing the gaps in treatment and trials goes beyond profit – it’s our moral duty.

And to unlock innovation at scale, policymakers should pair regulatory incentives with financing tools like targeted tax credits and dedicated grant programmes.

However, shrinking the gender health gap will take more than good intentions.

Clinical trials still face a range of issues, from under-reported side effects to low female participation. AI can help by improving how data is captured, making trials more inclusive and leading to fairer, higher-quality outcomes.

According to the National Centre for Biotechnology (NCBI), AI’s ability to analyse complex and diverse information allows for an improved understanding of gender differences, leading to more effective treatments for women in the future.

The World Economic Forum’s push to expand inclusion in clinical trials by involving more women highlights the importance of raising awareness among regulators and trial teams.

Inclusion must be prioritised in the early phases of trials, particularly in cardiology and oncology, and extended to underserved groups, including women of colour and post-menopausal women.

Attitudes to inclusion are also moving in the right direction, and AI can help drive that momentum.

By analysing large datasets, advanced algorithms can identify underrepresented women for targeted recruitment, leading to more representative trials.

AI can also review trial protocols to detect potential gender bias and recommend designs that address gender-based differences and women’s specific health needs.

Approach with caution

Although AI offers powerful capabilities, it’s not a fail-safe tool, and the need for human oversight has never been more important.

Models trained on biased data risk repeating existing inequalities, especially when sex-based differences in conditions like heart disease, stroke, and neurological disorders are still underrepresented in many datasets, leading to fatal consequences.

In doing so, AI could reinforce the very misconceptions about women’s health it’s meant to correct.

A study by the London School of Economics and Political Science (LSE) found evidence that AI tools are more likely to downplay women’s health issues compared to men’s. The increasing use of AI models by local authorities to supplement the workload of social workers across the country could result in widespread unequal care provision, perpetuating the gender bias.

The recommendation for researchers, clinicians, and developers to enhance sex-disaggregated data is especially relevant here.

Without data that clearly captures sex and gender differences, AI systems can’t be expected to recognise or respond to them accurately.

This requires standardised collection methods and consistent terminology so women’s health signals are properly captured and acted upon.

Healthcare organisations process huge amounts of data containing important clinical information, spread out and stored in different formats.

Improving the way data is captured makes clinical trials more inclusive and produces fairer, higher-quality results.

AI can combine structured and unstructured data, turning clinical records into meaningful and actionable insights.

AI regulation also varies wildly across countries.

The EU’s AI Act is one of the first major attempts to introduce clear rules, but elsewhere, regulation remains patchy.

While pharmaceuticals go through rigorous testing and approval processes, AI-driven tools often slip through regulatory gaps.

That’s why it’s even more important to design inclusive clinical trials from the start, ones that properly capture sex and gender differences and feed better data into these systems from the outset.

Mind the gap

Having reached crisis status, the task of reducing the gender healthcare gap can appear overwhelming.

But despite the scale of the challenge, there are real reasons for optimism.

We’re already seeing progress, and growing pressure from researchers, investors, and campaigners is pushing the system in the right direction.

But progress won’t come from technology alone. Transparency and inclusivity are just as important.

AI systems must be developed through processes that involve patients, clinicians, community advocates, as well as data scientists and engineers.

This kind of collaborative participation will help highlight blind spots, challenge assumptions, and build tools that reflect the complexities of healthcare, ultimately dispelling the one-size-fits-all myth.

If AI is going to play a role in closing the gender health gap, it must be guided by more than innovation.

Yes, we’re making technological breakthroughs, but if they simply replicate the inequities of the past, what use are they?