AI could bridge the women’s health gap, but better data is the key

Women’s health research and treatment has been chronically underprioritized - with serious individual and societal implications. From a practical standpoint, it makes the health of women across the world less predictable, and with more severe repercussions than for men. Meanwhile, from an economic standpoint, the loss is quantified in trillions of dollars.

The impact of the neglected women’s health comes to light most evidently on the level of individual experiences. Notably, women who come to a hospital with a severe heart attack are much more likely to get misdiagnosed than men, and women often struggle to get their conditions recognised and treated in a timely manner. This leads to avoidable suffering for the patients, as well as losses in productivity for companies and governments.

The data challenge

The potential contribution of artificial intelligence to women’s health is difficult to underestimate. The problem is that AI systems are only as good as the data they are trained on, and there are serious gaps in evidence and experience with regards to women’s health issues.

An evident problem stems from the fact that women have been poorly represented as research subjects. As a result, the data sets that are used to train diagnostic and prognostic systems tend to reflect men’s biology. When applied to women, such systems will be prone to errors, thus reinforcing healthcare disparities.

There are three general categories of women’s health issues across the globe. First, there are conditions that are uniquely present in women, such as endometriosis and menopause. Second, certain illnesses are more prevalent in women, a category that includes migraines and many autoimmune diseases. Finally, there are conditions that impact both sexes but in distinctly different ways, such as heart disease and diabetes.

Health problems specific to women

There are a number of health problems that are uniquely found in women. The main issue for such conditions as endometriosis and menopause is that they are understudied, meaning that little is known about their unique mechanisms. With regard to reproductive health, the situation is better in that there are fertility clinics that keep extensive data records about their patients. This data can be utilized to train machine learning algorithms to make more accurate predictions about women’s reproductive health outcomes. Where data is scarce, researchers could use synthetic data to train models to detect patterns that are relevant to women’s health.

For health problems that are common to women but not exclusively found in them, such as migraines and many autoimmune diseases, the issue is not an insufficient amount of data about those conditions. Instead, the challenge is that the data about migraines, for example, is not differentiated based on sex, which makes it difficult to spot patterns particular to women. The solution here, again, lies in using machine learning algorithms to find connections between such variables as sleep patterns or stress levels and female biology.

Regarding diseases that impact both sexes but do so in a different way

For diseases that impact both sexes but do so in a different way, the challenge is one of interpretation. Historically, much of the research data pertained to men, meaning that women’s health outcomes were not considered. With regard to diseases such as diabetes or heart disease, researchers and practitioners need to utilise sex-specific variables, such as miscarriages, to improve treatment outcomes for women. In the future, sex is expected to be a variable in most AI applications concerned with health.

Technology-enabled but not technology-driven

Closing the women’s health gap requires more than AI. It requires additional investment into women’s health research, sex-specific clinical trials, appropriate regulation, and more. Researchers need to make sure that women are represented in clinical trials, and that algorithms are sensitive to sex-specific variables in order to make accurate predictions. The healthcare industry can support clinical trials efforts, as well as utilise real-world evidence to identify patterns and develop better treatments. Governments should help in all of the above-mentioned areas by funding women’s health-specific research, providing regulatory guidance, and incentivising investment through clear pathways to approval. There is a substantial opportunity for women’s health to benefit from the rise of AI-driven healthcare but it would take time to realise. Whether it will happen soon enough will be determined by researchers, healthcare practitioners, the industry and lawmakers.