Multimodal health monitoring and theranostics based on functionalized hydrogels and artificial intelligence

Functionalized hydrogels are ideal flexible interfaces for multimodal health monitoring and integrated diagnosis-therapy systems, owing to their tissue-like mechanical properties, programmable biochemical functions, and hierarchical pores. However, practical applications are often limited by several material bottlenecks: mechanical fatigue and conductivity loss under cyclic stress, the mismatch between degradation rate and functional lifespan, and the trade-off between sensitivity and biocompatibility. To address these challenges, artificial intelligence (AI) has been applied to accelerate structural optimization and property prediction through molecular network engineering and inverse design. Meanwhile, during the collection of coupled mechanical and biochemical signals, these interfaces usually suffer from high background noise, data variability, and baseline drift. Machine learning and deep learning can process these complex datasets through noise filtering, automated feature extraction, and pattern recognition, enabling continuous monitoring and adaptive health management. This review summarizes the recent material design strategies of functionalized hydrogels, AI-driven data analysis methods, and their progress and challenges in integrated diagnosis and therapy.