How RPM Can Reduce AI ’s Bias Problem & Improve Health Equity

The following is a guest article by Arnaud Rosier, PhD, Founder and CEO at Implicity Artificial intelligence (AI) is one of the most promising breakthrough technologies of the modern healthcare era, yet it also has the potential to be one of the most dangerous. AI algorithms that are trained on limited or poorly representative data sets can exhibit signs of bias in their results, skewing decision-making and possibly leading to ethnic, gender, and social discrimination and other unintentional consequences for the patients they serve. Unfortunately, research shows that bias is already creeping into the nascent field of AI and machine learning.  In 2019, one study found that a widely used algorithm was underrepresenting the illness burden of black patients compared to white patients, meaning that Black individuals had to be much sicker to get a recommendation for the same level of care as their white counterparts. It was also well documented that Watson, IBM medical AI, was affected in many cases by bias, recommending therapies not accessible to the population using the software. Concerns over bias create distrust in AI and often keep healthcare leaders from fully embracing the technology. It is imperative that we address rising risks of AI bias before the ecosystem becomes even more established. We must find better ways of connecting with more diverse and representative patients to ensure trust by ensuring algorithms are trained with large and diverse datasets.  Remote patien...
Source: EMR and HIPAA - Category: Information Technology Authors: Tags: AI/Machine Learning Analytics/Big Data Clinical Health IT Company Healthcare IT Telemedicine and Remote Monitoring Academic Medical Center Ai Algorithm AI bias AMC Arnaud Rosier PhD Artificial Intelligence Dr. Arnaud Rosier HCP Source Type: blogs