Addressing barriers to clinical translation of body-fluid-sensing devices for chronic disease monitoring

Current diagnostic pathways rely on infrequent, laboratory-based sampling that is poorly aligned with real-time disease dynamics. Sensors for continuous or near-continuous biochemical monitoring offer a practical alternative. Wearables, integrated electronics, and lateral-flow devices enable decentralized analysis of biomarkers across body fluids, including interstitial fluid, sweat, saliva, tears, and urine. However, clinical translation remains limited by sparse validation, insufficient comparison to reference assays, and a lack of long-term performance data. Here, we discuss how specific body fluids dictate sensor architecture and performance and classify these systems as monitoring or diagnostic devices based on their regulatory claims and intended use. We further examine the role of artificial intelligence in biomarker correlation and performance optimization and outline key barriers to translating body-fluid-sensing technologies from laboratory settings to clinical and commercial deployment.