Wearable health technology has advanced dramatically in recent years, but a new development from Stanford University’s Digital Health Lab takes it a step further: an AI-driven smartwatch algorithm that can detect infections—including COVID-19, flu, and bacterial sepsis—up to 48 hours before fever or other symptoms manifest.
The system, detailed in Nature Digital Medicine, analyzes subtle changes in heart rate variability (HRV), skin temperature, and galvanic skin response (sweat levels) to identify early signs of infection. In a study of 5,000 participants, the AI correctly predicted 85% of febrile illnesses before users reported feeling sick. The algorithm was particularly effective in identifying COVID-19 cases during the Omicron wave, with a 90% detection rate.
One of the most promising applications is in hospital settings, where early fever detection can prevent sepsis-related deaths. A pilot program at Johns Hopkins Hospital equipped nurses with wearable sensors for high-risk patients. The AI flagged 70% of sepsis cases before clinical symptoms appeared, allowing preemptive antibiotic treatment and reducing mortality by 30%.
Critics raise concerns about false positives and data privacy, but developers are refining the algorithm to improve accuracy. Future versions may integrate with electronic health records, enabling real-time alerts to physicians. As wearable tech becomes more ubiquitous, AI fever prediction could revolutionize early diagnosis and outbreak containment.
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