When we talk about artificial intelligence (AI) revolutionising healthcare, the conversation often centres on speed, efficiency, and innovation. But too often, it misses a crucial point: technology alone won’t fix the systemic inequalities already embedded in medicine. Without deliberate action, AI risks amplifying the very biases that have historically sidelined women’s health.
Women’s Health: A Concerning Context
Women’s health has long been an afterthought in medical research and practice. The result? Conditions that disproportionately or uniquely affect women (like endometriosis or polycystic ovary syndrome) remain underexplored, underdiagnosed, and undertreated. On average, it takes seven years for a woman in France to receive an endometriosis diagnosis, a delay that compounds suffering and limits treatment options.
In its report Taking into account sex and gender to better treat: a public health issue (2020), the High Council for Equality between Women and Men warns about the impact of gender stereotypes in medical practice: persistent biases that can wrongly guide a diagnosis, delay it, or even prevent it, affecting both women and men.
Take the example of cardiovascular disease. It remains perceived as a “male” pathology in the collective imagination, typically associated with the image of a stressed, middle-aged man with a cigarette in hand. Thus, with identical symptoms, a woman complaining of chest tightness is three times more likely to be prescribed anxiolytics than a man, for whom the same clinical picture will more often trigger a referral to a cardiologist.
These inequalities do not only affect women. On the contrary, certain conditions are still perceived as “feminine”, such as depression or osteoporosis, and are consequently less often diagnosed in men.
These biases – often unconscious – partly originate in the training of healthcare professionals, which still remains largely centered on male or supposedly gender-neutral models, with few tools to deconstruct gendered representations. According to a study published in The Lancet in 2019, fewer than 25% of medical faculties include modules on sex- and gender-related differences in their curriculum. This lack of training results in a cycle of ignorance and stereotyping, with serious consequences for patients.
Social and Patient Biases
Gender biases, linked to the social representations of girls and women, are not limited to the medical field: they often begin within the family circle and shape the first perceptions of children’s behaviour. For example, in young children with autism spectrum disorders, withdrawal and lack of social interaction are more likely to be mistaken for shyness and reserve in girls. These same behaviours in boys are more often interpreted as a sign of communication disorder, since they deviate from social expectations of boys’ behaviour as more expansive and dynamic. As a result, parents and teachers are more likely to seek medical consultation for boys than for girls.
Delays in diagnosis can also stem from patients themselves. 70% of women report consulting a doctor only when they have no other choice, often because they prioritise the health of their loved ones. This tendency is compounded by the minimisation of symptoms. As women are accustomed to experiencing “normal” pain throughout their lives, from menstruation to childbirth, they may downplay other forms of pain, such as chest pain. A study by the European Society of Cardiology showed that women take, on average, 37 minutes longer than men to call emergency services during a heart attack.
Diagnosis may also be delayed by the concealment of symptoms. In Alzheimer’s disease, for instance, women’s generally stronger language skills allow them to mask the early signs of the condition for longer, delaying recognition and treatment. A similar pattern appears in autism: unlike autistic men, many autistic women develop advanced imitation and social adaptation skills, allowing them to hide their difficulties. This camouflaging can delay, or even prevent, recognition of their condition. The cost, however, is significant psychological fatigue, and their difficulties, though real and disabling, are often perceived as less marked, increasing the risk of underdiagnosis or misdiagnosis.
AI: A Powerful Tool for Improving Women’s Health
AI offers tools that could begin to close these gaps, if developed and applied responsibly. For example, AI-assisted imaging is already cutting reading times in breast cancer screening by 13%, enabling earlier detection and intervention. New genomic-AI models promise to spot endometriosis before symptoms fully emerge. Startups are leveraging AI to personalise cancer treatments, tailoring therapies to individual genetic and biological profiles, something particularly critical given the differences in how men and women respond to treatment.
But here’s the catch: AI is only as good as the data it’s trained on. And historically, that data has underrepresented women. From clinical trials that included only 20% women to diagnostic algorithms trained predominantly on male patient records, we’ve created technologies that inadvertently carry forward a male-centered view of health. As UK officials have acknowledged, even cutting-edge AI models in liver and kidney disease often perform better for men than women because of this skewed training data. In other words, the “neutral” datasets we celebrate are anything but neutral.
This isn’t just about data, it’s about who builds the technology. Only about a quarter of the AI workforce is female. Without diverse perspectives shaping the design and testing of these tools, blind spots will persist. AI doesn’t invent bias; it reflects and reproduces the inequities of the system that created it.
The good news is that steps are being taken. Efforts like the UK’s Standing Together initiative are working to set inclusive standards for AI datasets, ensuring they reflect diverse patient populations. AI is also being harnessed to improve women’s representation in clinical trials, by rapidly filtering patient profiles for diversity and flagging potential biases in study design. These moves are essential, but they must become the rule, not the exception.
The future of women’s health, and indeed, the credibility of AI in healthcare, hinges on trust. To build it, we must ensure AI technologies don’t just deliver faster diagnoses and more efficient care, but that they do so equitably. That means integrating sex and gender differences into research, diversifying the workforce developing these tools, and holding developers accountable for bias.
AI can be a powerful catalyst for change. It can shorten the road to diagnosis, personalise care, and finally make women visible in medical data and research. But let’s be clear: technology won’t automatically dismantle years of bias. That requires deliberate, systemic action. AI, in this sense, is not the solution, it is a tool. Whether it helps bridge the gender gap in healthcare or widens it will depend on how responsibly we choose to utilise it.
Sources
https://www.gov.uk/government/speeches/the-role-of-ai-in-the-future-of-womens-health
https://www.alcimed.com/en/insights/gender-bias-in-medical-diagnosis/