Summary:
«Historically, healthcare data has been focused on white men, and in the age of artificial intelligence (A.I.), this represents a challenge to train the algorithms to deliver results that are representative across the ethnic and gender spectrum. Given that existing data leans towards the white male subset of the population, this will inevitably lead to ‘algorithmic bias’ in healthcare. The latter term is what researchers define as the instances when the application of an algorithm does not account for inequities but may in fact exacerbate them in healthcare systems.»
«Indeed, researchers have found that inherent biases in data can amplify health inequities among racial minorities. We also covered the topic of A.I. bias in healthcare at The Medical Futurist. And while it is crucial to raise awareness of this aspect of smart algorithms, it is equally important to know about measures that can be undertaken to eliminate, rather than avoid, biases as A. I. increasingly become an integral part of the healthcare landscape.»
Article written by Medical Futurist
07|04|2022
Source:
Medical Futurist
https://medicalfuturist.com/4-approaches-to-eliminate-bias-in-healthcare-a-i/