In our new paper, we describe a framework for continual monitoring and updating of AI algorithms in healthcare. Read it today on npj Digital Medicine and share your thoughts.
No organization by itself, including the FDA, has the expertise to oversee the use of clinical AI.
It’s hard enough to prevent algorithmic bias when one can see all the data. It’ll be impossible to detect and mitigate bias when only the weights are shared during the modeling.
Over the past 3 decades, the incidence of AKI has increased over 20-fold, making it an important problem in critical care medicine. The purpose ofthis paper was to investigate the complex factors mediating the relationship between urine output and creatinine in AKI, and to develop a time varying multivariable model that identifies factors mediating the relationship based on augmentation of urine output with physiological features.
Effective response to a pandemic requires policy makers and professionals to acknowledge and examine associated challenges such as the absence of evidence-based tests and treatments and the reassignment of healthcare staff to unfamiliar work environments.