Artificial intelligence (AI) is rapidly reshaping how healthcare advice is delivered. From symptom checkers and diagnostic tools to risk models and care-navigation platforms, AI is becoming a first port of call for millions of patients. That influence is growing fast. Around 24% of UK adults report seeking health advice via AI tools or social platforms. But as adoption accelerates, an uncomfortable truth is coming into focus: AI is only as good as the data it learns from, and in women’s health, that data has long been incomplete.
From data gaps to diagnostic risk
Women make up half the global population, yet healthcare systems have rarely been designed around their lived experiences. The recent Kearney white paper “Prescription for Change”, in collaboration with the World Economic Forum shows that only 7% of global healthcare research funding is directed towards conditions that exclusively affect women. Just 5% of medications are properly tested and labelled for use during pregnancy or breastfeeding. How women’s bodies respond to illness and treatment remains underrepresented across research, trials and real-world datasets.
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When AI systems are trained on this skewed foundation, the consequences are immediate. Symptom-triage tools may underweight pain or fatigue, symptoms women are more likely to report, leading to genuine concerns being misclassified or deprioritised. Diagnostic algorithms can also perform less accurately for women, particularly in areas such as heart disease, stroke and cancer risk assessment.
Generative AI is no exception. Research from the London School of Economics and Political Science (LSE) found evidence that AI tools are more likely to downplay symptoms in women and ethnic minorities. If deployed at scale, that bias risks shaping care prioritisation and access in ways that are hard to detect and even harder to undo.
This is not a failure of technology. It is the predictable outcome of building advanced systems on incomplete foundations.
Why sex-disaggregated data is non-negotiable
Policymakers are beginning to recognise the risk. In early 2025, the UK Minister of State for Women’s Health warned that without representative data, AI could entrench disparities rather than reduce them.
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By GlobalDataIf AI is to support safe, effective care, collecting sex-disaggregated data must become routine, not optional. Systems need to reflect biological differences and track women’s health across the life course. That requires standardisation: consistent definitions, interoperable frameworks and data that can be meaningfully analysed across services and settings.
Ensuring sex-disaggregated data isn’t just a question of equality, it is also essential to performance and sustainability. AI models trained on robust, representative datasets are more accurate, more reliable and more clinically useful. As regulators and providers increasingly scrutinise real-world outcomes, organisations that invest in data quality now will be better placed to demonstrate safety and value later.
Scaling women’s health services without scaling inequality
Technology offers a real opportunity to narrow the gender health gap, but only if it is used to improve fairness and representation, not just efficiency. Data, digital platforms and AI are already shaping how clinicians are trained, how evidence is generated, and how care is delivered at scale. Designed with equity in mind, these tools could help correct long-standing blind spots in women’s health rather than reinforce them.
However, the systems they are being introduced into remain deeply fragmented. From menstruation to maternity and through to menopause, women’s care pathways are inconsistent, poorly connected and chronically under-resourced. The consequences are already visible: gynaecology is now the largest specialty on NHS waiting lists, accounting for 12% of patients aged 18–64.
What is needed is a joined-up approach that works across the life course. That means investing in standardised pathways and scalable women’s health hubs. Crucially, those systems must be built with women, not simply for them. Lived experience should shape design from the outset, through genuine collaboration between patients, clinicians, community advocates and data scientists.
The risks extend beyond service delivery into research and innovation. A study by the Medicines and Healthcare products Regulatory Agency and the University of Liverpool revealed that male-only clinical trials outnumber female-only trials by 67%. Those evidence gaps ripple through clinical guidance and into AI development. While AI could support more inclusive trial design and analysis, without reform and clear regulatory direction, it will simply mirror existing exclusion.
What needs to change
AI has the potential to be a decisive force in closing the gender health gap, but only if it is applied with intention. Left unchecked, it risks embedding historic blind spots into new digital systems.
As highlighted in Kearney’s HealthTech Manifesto in partnership with Microsoft, removing these risks requires more than improved technology. It demands clear guardrails around how data is collected, how systems are designed, and how innovation is deployed. Sex-disaggregated data must become the default, not the exception, and organisations must question the accuracy of legacy datasets before scaling them into AI-related services.
Equally important is how new systems are built. Digitising fragmented pathways without redesigning them will only accelerate existing weaknesses. Technology should be used to reimagine women’s health services across the life course, replacing disconnected models with joined-up, women-centric care that reflects real biological and social complexity.
Design also matters. Building systems with women – rather than for them – leads to more representative data, more relevant tools, and greater trust in digital care. Without lived experience shaping innovation from the outset, even well-intentioned solutions risk missing the realities they are meant to address.
AI will play an increasingly central role in healthcare delivery. Whether it entrenches long-standing inequalities or helps dismantle them will depend on the choices made now.
