AI-enabled wearable microfluidics for next-generation infection monitoring and therapeutics

Abstract

Wearable biosensors have revolutionized healthcare by enabling continuous, minimally invasive monitoring of health parameters. While traditional wearables primarily measure physiological signals, recent advancements now allow biochemical sensing of microbial biomarkers across diverse human biofluids, including sweat, saliva, wound exudate, interstitial fluid, tears, breath, and urine. These biomarkers, including microbial nucleic acids, metabolites, and host immune mediators, provide valuable information for diagnosing and managing infections. Wearable microfluidic devices are designed to sample these biofluids directly from the body and allow for rapid identification of microbial signatures and associated host responses. Moreover, some wearables' use of living microorganisms as functional components has opened new opportunities for biosensing and therapeutic delivery. The integration of artificial intelligence improves the interpretation of complex and dynamic data streams, and facilitates precise and adaptive decision-making. Additionally, by addressing biomechanical interactions between microorganisms, host tissues, and wearable interfaces, mechanomedicine principles provide insights into these systems. In the near future, these interdisciplinary innovations have the potential to transform infection control, personalized healthcare, and global health surveillance.