At many American hospitals, bedside monitors that measure everything from patients’ intracranial pressure to heart, respiration and blood pressure rates feed numbers into artificial intelligence systems that constantly assess their risk of suffering things like stroke, sepsis and heart attack.
But because the algorithms that feed such predictive health care AI systems are often based on data from homogenous populations, AI that’s meant to improve care for everyone sometimes falls short – and may even prove harmful.
Supported by a new $5.9 million National Institutes of Health grant, two University of Virginia researchers will explore ways to improve the use of artificial intelligence in health care for a wider diversity of patient populations.
Ishan Williams, an associate professor in the School of Nursing, and Randall Moorman, a UVA Health cardiologist, will lead the multi-institutional research effort. (Contributed photos)
Primary co-investigators Ishan Williams, an associate professor in the School of Nursing, and Randall Moorman, a UVA Health cardiologist, will develop, test and deploy best practices for artificial intelligence health care systems that aggregate data from a more diverse pool of patients by taking into account their race, ethnicity, socio-economic status and geography. Moorman is the Bicentennial Professor of Advanced Medical Analytics in the School of Medicine and a well-known expert on artificial intelligence who pioneered a continuous monitoring tool for both NICU patients and on COVID-19 units used around the world.
