cardiac arrest
Researchers at Johns Hopkins found that the AI outperformed current clinical guidelines with 89% accuracy across all patients and a 93% accuracy rate for individuals ages 40 to 60.
The device predicts cardiac arrests using four primary vital signs.
By listening for agonal respirations, an algorithm described in a recent NPJ Digital Medicine paper offers a path for smartphone- and personal assistant-based heart attack detection.