How racial biases in medical algorithms lead to inequities in care Dec 17, 2022 5:35 PM EDT. Leave your feedback. Share. Copy URL.

For example, excluding race from algorithms that predict the future survival of patients with kidney failure would fail to identify those with underlying circumstances that make them more vulnerable.

When developers make algorithms, they often use “training data,” or past data sets, to train the algorithm to associate certain health outcomes with certain types of people. For example, the algorithm ...

AI in medicine need to counter bias, and not entrench it more : ... An algorithm that could predict the threat of sepsis in kids would be a gamechanger for physicians across the country.

Hello and welcome to Eye on AI. In today’s edition…An international initiative aims to tackle bias in medical AI algorithms; ...

Some algorithms used in the clinical space are severely under-regulated in the U.S. The U.S Department of Health and Human Services (HHS) and its subagency the Food and Drug Administration (FDA) are ...

My broad takeaway: Algorithms cannot be trusted to make safe and fair decisions about patient care. advertisement The timing is critical, given the rise of generative AI.

A replacement for race: Medical experts explore how to eliminate bias in clinical algorithms. By Lizzy Lawrence and Katie Palmer June 28, 2023.