AI improves stroke diagnosis accuracy, say researchers.

Researchers from Carnegie Mellon University, Florida International University, and Santa Clara University have developed a machine learning model that predicts strokes with 84% precision, outperforming existing scales that miss as many as 30% of strokes. The model incorporates variables routinely collected by healthcare providers and payers, including basic demographics, the number of chronic conditions, and insurance. The study's authors suggest that it is possible to predict the likelihood of a patient's condition being a stroke at the time of hospital presentation, based on patients' demographics and social determinants of health available at the time of entry.