There’s a great deal of buzz around artificial intelligence and its potential to transform industries. Healthcare ranks high in this regard. If it’s applied properly, AI will dramatically improve patient outcomes by improving early detection and diagnosis of cancer, accelerating the discovery of more efficient targeted therapies, predicting disease progression, and creating ideal personalized treatment plans.
Alongside this exciting potential lies an inconvenient truth: The data used to train medical AI models reflects built-in biases and inequities that have long plagued the U.S. health system and often lacks critical information from underrepresented communities. Left unchecked, these biases will magnify inequities and lead to lives lost due to socioeconomic status, race, ethnicity, religion, gender, disability, or sexual orientation.
