Why Innovation is Important for Femtech Sector to Grow

Why Innovation is Important for Femtech Sector to Grow

Healthcare Tech Outlook | Wednesday, February 17, 2021

The healthcare sector is using artificial intelligence to improve female healthcare as it can diagnose efficiently. 

FREMONT, CA: The economies, which are developing, evolving, and advanced, investing in women's health will minimize healthcare costs and allow greater use of current health resources for the good of all members of society.

Most health research has traditionally been performed either on men or, more recently, without considering differences among the sexes. In some cases, this has resulted in less successful medications for women and the misdiagnosis of severe health conditions for women, like heart attacks, because their symptoms can vary from those of men. It is time to take a different approach to the well-being of women.

Femtech isn't niche. Half of the world is female.Top Artificial Intelligence Solution Companies

At present, Femtech includes everything from period trackers to genetic testing that reveals the risk of endometriosis and other fertility disorders. The purpose of Presagen is to apply artificial intelligence (AI) to the health of women.

Life Whisperer, their first product, enhances the selection of embryos in IVF by at least 25 percent and helps decrease the number of cycles required to attain pregnancy. In the fertility market, they are also focusing on other AI products, early diagnosis, and treatment of female incident cancers, like breast, ovarian, and cervical cancer.

Realizing that certain diseases occur differently in women (heart disease is an example) or are more frequent in females, such as Alzheimer's disease, the chance of femtech advances is essential.

Good femtech AI is trained on globally diverse data.

Humans are in an age where AI's implementation to medical research and current patient records can significantly enhance preventive and diagnostic medicine and define the best care plans for every patient for the first time.

Training the AI on a data set that is mostly or heavily biased toward the male gender will continue the trend of inadequate results for women. A less dependable product will result from training data from women through one clinic or even one region. It will only apply to comparable patients if demographics distorts the data.

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