Women’s health is important to all of us regardless of our gender.
In all economies – developing, emerging and advanced – investing in women’s health can reduce healthcare costs and make better use of existing health services to the benefit of all members of society.
Historically, most health research has been conducted either on men or, more recently, without any consideration of sex-based differences. In some instances, this has resulted in medicines that are less efficacious for women and the misdiagnosis of women’s serious health issues such as heart attacks, because their symptoms can differ from men’s. It is time we take a radically different approach to women’s health.
Femtech isn’t niche – Half of the world is female!
Femtech currently encompasses everything from period trackers to genetic testing that indicates the likelihood of endometriosis and other fertility disorders. My company Presagen is focused on applying artificial intelligence (AI) to women’s health. Our first product, Life Whisperer, improves embryo selection in IVF by at least 25% and in doing so is helping to reduce the number of cycles needed to achieve a pregnancy. We are also working on other AI products in the fertility sector, as well as the early identification and treatment of female incident cancers, such as breast, ovarian and cervical cancer.
Knowing that some diseases present differently in women, (heart disease being an obvious example) or are more prevalent in women, like Alzheimer’s disease, the opportunity for advancements in femtech is significant.
Good femtech AI is trained on globally diverse data
We are in an era where the application of AI to medical research and existing patient records has the potential to improve preventative and diagnostic medicine dramatically and identify first-time the optimal treatment protocols for each patient.
However, just as training the AI on a data set that is wholly or heavily skewed towards the male gender will continue the pattern of poorer outcomes for women, training on data from women from one clinic, or even one country, will result in a less reliable product. With the data skewed by demographics, it will only have relevance to equivalent patients.
At Presagen, we work collaboratively with and for clinics worldwide. Sourcing globally diverse data, we remove the bias associated with individual clinics, whether it is related to patient demographics or the clinical environment. This means our AI model built on this data can be generalised to other clinics and result in robust clinical tools that benefit all womankind.
If you would like to collaborate on our latest projects or learn more about our work, visit presagen.com