How Augmented Intelligence Can Promote Health Equity
Data sets that train artificial intelligence and machine learning technology may not be representative of the population as a whole, studies have revealed. In this Healthcare IT News interview, he draws from his technical and clinical experiences to explain how to train models on more diverse image and data sets, and why he believes this strategy is key for providing clinicians with reliable and equitable resources that augment decision-making, overcome knowledge gaps, and promote greater health equity and outcomes. Q. Studies reveal that data sets used to train artificial intelligence and machine learning often lack representative data. A. Training sets for artificial intelligence and machine learning technology are often determined by the demographic of the geographically based health system. For example, health systems operating in areas without large communities of patients of color may lack representative data to adequately train models to treat such populations. Developers of machine learning models should strive to access training sets from diverse organizations. Health systems implementing these technologies must understand the patient base they are developed from, and the criteria of the test sets used to measure the accuracy of the machine learning models. Healthcare professionals have numerous and rigorous ways to evaluate the accuracy of these models, such as developing a test set that represents different populations. Q. Based on your technical and clinical experiences, please explain how to train models on more diverse image and data sets. A challenge with collecting and training diverse image sets are the many pigmentation differences in skin.