Gender bias in health algorithms

A 2019 Today & Survey Monkey poll¹ highlighted that 52% of women experienced gender discrimination in healthcare. This is compared to only 36% of men who felt any discrimination.

These statistics are particularly alarming, since AI innovation is emulating real-life paradigms. Women have frequently struggled to prove the legitimacy of their health concerns. They are often being dismissed or having to sift through several healthcare providers before they feel that their health concerns are receiving any attention.

Such reports are particularly useful when going into innovation. However, are the initial phases of AI use already showing signs of bias?

Does gender really influence patient outcomes?

This disparity in healthcare can be seen in health conditions like diabetes, where it takes 4.5 years longer for women to get diagnosed compared to men. A later diagnosis for women has been observed in over 700 diseases in a population-wide analysis² of disease progression among men and women.

Gender bias in healthcare has influenced not only a delay in treatment but often includes misdiagnosis, inadequate care, and even limited access to healthcare for women.

This probably started with the fact that women have been underrepresented in clinical trials. In 1993, it was mandated by law³ that women be included in trials. However, many factors still limit complete understanding of health conditions, as women who are pregnant or in perimenopause may not be included in most studies.

The underrepresentation in clinical trials affects how data on many diseases are observed in women. For instance, symptoms of a myocardial infarction can be very different for women, often presenting as fatigue, nausea, and back pain. Classic symptoms such as chest pain, breathlessness, or radiating pain to the neck or left arm are more commonly recorded in studies conducted on men.

If these biases are used to build AI systems, there is a risk of amplifying the existing inequalities.

Are women underrepresented in AI?

Women are currently being left out, both in the AI workforce and the systems being built. This gender imbalance contributes directly to the biases that emerge in AI applications, especially those used in sensitive areas like healthcare.

Women were observed to make up only 30% of the generative AI workforce⁴, according to a 2024 Deloitte review on women and generative AI. This review also highlighted that women were half as likely to adopt generative AI tools compared to men. While parity in usage is on the horizon, this lack of gender diversity among AI use means many algorithms are being trained without adequate consideration for gender-related nuances.

In healthcare, this limitation is particularly dangerous. Clinical trials and health records often skew toward male data sets, leading AI systems to learn patterns based on predominantly male data. An immediate outcome of this would mean that algorithms would not be able to diagnose and treat diseases specific to women.

To reduce this bias in generative AI, more women must be included in all aspects of AI development, from research and engineering to data science and ethics. At the same time, while still in its infancy, datasets must be audited and diversified, ensuring AI systems are truly equitable.

What are the challenges facing women with the adoption of AI in healthcare?

Adopting AI models can have many positives when considering that accuracy in diagnosis can improve. For one, conditions such as endometriosis and breast cancer, which often take years to diagnose, may in the future be diagnosed at treatable stages, reducing disease morbidity and mortality. AI models may also encourage a more individualistic approach to management based on specific criteria, such as hormones, disease progression, and genetic makeup.

However, before we reach such a stage, there are many aspects that require looking into when training AI models for women’s health.

Gender-biased medical data

Clinical trial datasets, medical imaging databases, and electronic health records are currently more skewed toward male physiology. Even treatment options are likely to be recommended based on symptoms catalogued in men. AI tools might be less accurate in treating conditions, especially with atypical symptoms, for women.

Misdiagnosis and delayed care

Current AI-driven diagnostic tools may perpetuate historical gender biases based on flawed past clinical practices. For instance, pain-related conditions such as endometriosis or autoimmune diseases are often misdiagnosed or dismissed in women. If these patterns are used as training data, AI systems will likely replicate them, delaying appropriate care and worsening outcomes.

Lack of gender-aware design

Since treatment has historically been a “one-size-fits-all” approach, AI tools are also being trained similarly. This ignores the gender-specific differences in anatomy, hormone cycles, drug metabolism, and disease progression. Without a gender-aware design, AI may offer recommendations that are ineffective or, worse, harmful to women.

Digital health access and literacy gaps

As we have seen, women are currently adopting AI technology at a slower pace than men. Many factors can contribute, many already affecting women’s access to technology, such as limited device access, a deficiency in education, and limitations in autonomy with decisions. This, in turn, will impact how AI health platforms are also adopted. With the pace at which technology is advancing, a large percentage of women may be left behind in terms of access and literacy, not to mention an inability to benefit from AI-enabled healthcare.

How is this disparity with AI going to further impact healthcare for women?

Without much change, the use of biased AI tools in healthcare could widen existing gender disparities in medical outcomes.

A 2019 Guardian article⁵ revealed that an AI system used to predict healthcare needs in over 200 million patients significantly underestimated the severity of illness in African American patients compared to Caucasian patients. While this article highlights racial bias, this inadvertently shows that non-medical indicators, such as gender, can skew clinical decisions.

This means, for women, a future where AI could misclassify symptoms, fail to flag early warning signs of disease, or deprioritize their care in emergency settings. For example, if an algorithm used for ER triage is trained on male-centric data, a woman presenting with “atypical” symptoms may be dismissed as low-risk, delaying treatment and worsening the outcome.

Women, additionally, are more prone to chronic conditions such as autoimmune diseases. With limited or skewed datasets for these conditions, there could be more delays in management.

Without intentional intervention, AI may embed gender disparities more deeply into healthcare infrastructure, turning these individual biases into systemic flaws. The consequence of this could be dire, where women are further impacted by careless implementation.

How can we build AI healthcare tools to be more inclusive?

To ensure AI in healthcare serves everyone equitably, we would have to go beyond gender-neutral models. This means actively creating gender-aware AI models from the ground up.

First, we would need representative datasets. A Nature Digital Health discussion on the sex and gender biases and differences⁶ used to train AI models highlights how the use of existing data could essentially be a double-edged sword, either improving or worsening existing bias. Precision medicine with AI, in essence, eliminates the one-size-fits-all thesis of healthcare. However, if biases are not tackled early, they can worsen an already flawed system further, negating the intended outcome of personalized care from gender bias.

Second, AI healthcare tools must undergo regular gender-bias audits. This should include testing model performance and monitoring outcomes over time. Health regulators can support this by being a part of these trainings and implementing transparency that goes into the development.

Third (or maybe the first) would be to include women in the AI development process. This means fostering interdisciplinary collaboration between technologists, clinicians, social scientists, and patient advocates of all backgrounds, races, genders, and abilities to surface needs that may be overlooked.

Additionally, having gender-trained AI models can help better understand how various factors influence health outcomes, as seen with initiatives like the Gendered Innovations project by Stanford University⁷.

To wrap up

Inclusive AI healthcare tools are not just ethical; they are effective. They result in equitable diagnoses, treatments, and healthcare, ensuring technological progress benefits everyone.

The underrepresentation of women in all aspects of healthcare, starting from clinical data to the dispensing of treatment, contributes to a system that often treats male healthcare as the default. The result when training AI? Algorithms that perform well for men but fail when treating women.

Tackling gender bias in healthcare AI is crucial in these early stages. Technology should enhance equity, not entrench further inequality. Ensuring women are seen, heard, and adequately cared for in the age of AI is essential to building a truly equitable healthcare system.