Researchers examine uncertainty in medical AI papers going back a decade

In the big data domain, researchers need to ensure that conclusions are consistently verifiable. But that can be particularly challenging in medicine because physicians themselves aren't always sure about disease diagnoses and treatment plans. To investigate how machine learning research has historically handled medical uncertainties, scientists at the University of Texas at Dallas; the University of California, San Francisco; the National University of Singapore; and over half a dozen other institutions conducted a meta-survey of studies over the past 30 years. They found that uncertainty arising from imprecise measurements, missing values, and other errors was common among data and models but that the problems could potentially be addressed with deep learning techniques. The coauthors sought to quantify the prevalence of two types of uncertainty in the studies: structural uncertainty and uncertainty in model parameters.