AI-driven next-gen health monitoring via flexible sensors to decode breath

Respiratory dynamic patterns offer a unique window into the body's cardiorespiratory, metabolic, and neurological functions, yet continuous and accurate capture of such signals is fundamentally challenged by the mismatch between sensing interfaces and dynamic biological tissues. This review focuses on the convergence of flexible material innovations and artificial intelligence (AI) analytics to outline the development of next-generation intelligent respiratory monitoring. It systematically examines bioinspired flexible strain and humidity sensors with microstructural designs, elaborating on their medical-grade accuracy in quantifying respiratory parameters and the central role of material-device co-design in boosting sensing performance, comfort, and robustness. AI's function as a powerful signal decoder, enabling at-home lung function measurement, anomaly detection, and cross-disciplinary digital biomarker extraction for disease screening, is discussed. The review constructs a material-to-integration-to-intelligent interpretation technological paradigm advancing personalized medicine, envisions its value in chronic disease management and remote monitoring, and identifies key clinical translation challenges to provide forward-looking research perspectives.