Is AI closing the gender health gap - or making it worse?

Artificial intelligence (AI) stands perched on a precarious tightrope when it comes to closing the gender health gap. 

On one side lies the promise of revolutionary tools that could finally address centuries of gender-based healthcare disparities. On the other, the risk that these same tools might unknowingly amplify the very inequities they aim to solve. 

The central question we face is whether AI will ultimately balance these competing forces to close the gender health gap or inadvertently widen it through technological blind spots.

Consider the potential of AI models in diagnosing conditions often overlooked in women’s healthcare, such as heart attacks. Women experiencing heart attacks are frequently misdiagnosed or dismissed by healthcare providers.¹ This may be because their heart attack symptoms often differ from "classic" male presentation - in some cases, they may experience fatigue, nausea, or jaw pain which are not always widely recognised as “typical” heart attack symptoms.² Machine learning algorithms are now being trained to analyse medical data from CT scans, electrocardiograms (ECGs), and blood tests, to identify people at high risk of heart attacks before they even occur.³ This could potentially transform diagnostic accuracy for women, addressing longstanding gaps in healthcare that stem from historical underrepresentation.⁴

So what’s the catch? 

Many models are trained predominantly on majority male datasets. 

As a result, when these models try to make predictions for people who were underrepresented in the datasets they were trained on - often women - they may misdiagnose them.⁵ Without fair and inclusive data inputs, even the most advanced AI systems can go off course. This highlights our precarious balancing act on the healthcare tightrope: AI, hailed for its potential to eliminate human error, risks becoming the very thing that perpetuates age-old biases.

A call to action: data representation and model verification

At the recent Responsible AI: Women & Healthcare Conference, hosted by the Department of Health and Social Care and The Health Foundation, experts gathered to dissect the delicate balance between the risk and reward of AI in women's healthcare.⁶  

A core recommendation emerged: we must fundamentally improve the quality and diversity of data that AI systems are trained on. 

In other words, we need to deliberately incorporate disaggregated data - data that separates results by sex - and conduct thorough subgroup analyses across gender, ethnicity, and other demographic factors to develop more effective and personalised healthcare solutions.

Before AI tools become widely adopted in healthcare settings, rigorous verification processes must be established to ensure their efficacy and safety across diverse populations. These safeguards aren't just bureaucratic hurdles - they're essential checks to prevent algorithms from inadvertently cementing existing healthcare disparities. 

By designing verification systems that reflect the complex realities of patient populations, we can build AI tools that are built on transparent development processes and genuinely inclusive design. 

Building trust is essential - especially as we know that a “trust gap” is emerging, where women express more distrust of AI systems than men.⁷ 

The potential of AI-driven solutions in women’s health

Despite the challenges, promising AI applications in women's health continue to surface.

For example, innovative pattern recognition models are shedding new light on Polycystic Ovary Syndrome (PCOS), enhancing patient outcomes, streamlining clinical decision-making, and minimising potential diagnostic delays.⁸ Research also shows promise for using AI to analyse risk factors and identify women at risk of endometrial cancer (cancer of the lining of the uterus) - offering significant advantages over current screening approaches that rely heavily on patients recognising and reporting their own symptoms.⁹

Beyond predictive modeling, AI can offer meaningful ongoing support and patient monitoring across women's healthcare journeys. AI-powered health assistants specifically designed for women's health concerns could provide accessible information, personalised treatment recommendations, and clear guidance on when to seek professional care. These virtual “co-pilots” could help bridge critical knowledge gaps, particularly for sensitive topics that women may hesitate to discuss even with healthcare providers. 

By offering judgment-free information and support, these AI tools can empower women to better understand their bodies and make more informed healthcare decisions.

The path forward: moving beyond awareness to action

One salient insight from the conference is that the AI community must transcend its honeymoon phase. 

Awareness of AI bias is only the beginning. 

The tech industry now needs tangible steps to turn AI into a true partner for healthcare equity, rather than an unintentional obstacle. What's truly needed is a fundamental shift in how we develop healthcare AI - moving away from retrofitting "fairness" onto existing systems and instead building equity into the foundation of these technologies from day one.

As AI continues to balance on a tightrope that could either bridge significant gaps in women's health or deepen them further, the direction it tips will depend on immediate, concrete interventions: investing in diverse data sources, implementing rigorous verification processes focused on demographic equity, and demanding transparency in AI development.

As we integrate AI into healthcare, let's ensure it truly serves everyone - particularly those historically overlooked by medical systems. 

This isn't just about better technology; it's about finally addressing centuries of gender disparity in healthcare through tools specifically designed to overcome, rather than reinforce, these persistent inequities.

The numan take 

As we forge ahead with the responsible use of AI in healthcare, Numan is dedicated to tackling biases within AI models that can overlook female patients. Recognising the impact of data bias on women's healthcare, we envision a future where AI is tailored to the unique needs of women by considering biological and societal factors, leading to more effective treatments. Through the creation of female health-focused virtual assistants, we aim to develop tools that are fair, inclusive, and empowering and ultimately help close the gender health gap. 

References

1. ^{O’Connor BA. Why Heart Disease in Women Is So Often Missed or Dismissed. Nytimes.com. 2022. [accessed 24 Sept 2025] Available from: https://www.nytimes.com/2022/05/09/well/live/heart-disease-symptoms-women.html}

2. ^{Lichtman JH, Leifheit EC, Safdar B, Bao H, Krumholz HM, Lorenze NP, et al. Sex differences in the presentation and perception of symptoms among young patients with myocardial infarction: Evidence from the VIRGO study (Variation in Recovery: Role of Gender on Outcomes of young AMI patients): Evidence from the VIRGO study (Variation in Recovery: Role of Gender on Outcomes of young AMI patients). Circulation. 2018;137(8):781–90.}

3. ^{Doudesis D, Lee KK, Boeddinghaus J, Bularga A, Ferry AV, Tuck C, et al. Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations. Nat Med. 2023;29(5):1201–10.}

4. ^{Women’s health research lacks funding – these charts show how. Nature.com. 2023. [accessed 24 Sept 2025] Available from: https://www.nature.com/immersive/d41586-023-01475-2/index.html}

5. ^{Zhao AP, Li S, Cao Z, Hu PJ-H, Wang J, Xiang Y, et al. AI for science: Predicting infectious diseases. J Saf Sci Resil. 2024;5(2):130–46.} 

6. ^{Department of Health, Social Care. The role of AI in the future of women’s health. Gov.uk. 2025. [accessed 24 Sept 2025] Available from: https://www.gov.uk/government/speeches/the-role-of-ai-in-the-future-of-womens-health}

7. ^{Women and generative AI: The adoption gap is closing fast, but a trust gap persists. Deloitte Insights. Deloitte; 2024. [accessed 24 Sept 2025] Available from: https://www.deloitte.com/us/en/insights/industry/technology/technology-media-and-telecom-predictions/2025/women-and-generative-ai.html}

8. ^{Upreti K, George O, Upreti S, Mahajan S. Polycystic ovary syndrome diagnosis: The promise of artificial intelligence for improved clinical accuracy. Biomed Pharmacol J. 2025;18(1):353–72.}

9. ^{Erdemoglu E, Serel TA, Karacan E, Köksal OK, Turan İ, Öztürk V, et al. Artificial intelligence for prediction of endometrial intraepithelial neoplasia and endometrial cancer risks in pre- and postmenopausal women. AJOG Glob Rep. 2023;3(1):100154.}