Multimodal Health Monitoring and Theranostics Based on Functionalized Hydrogels and Artificial Intelligence

Abstract

The integration of functionalized hydrogels and artificial intelligence (AI) is driving the transformation of medical models toward real-time, personalized diagnosis and therapy. Leveraging their tissue-like modulus, programmable functionality, and porous network structures, hydrogels have emerged as ideal platforms for flexible, multimodal physiological data acquisition, encompassing mechanical signals and biochemical markers. However, data generated from these platforms exhibit significant multimodality, heterogeneity, and high noise levels, rendering traditional analytical methods inadequate for extracting robust physiopathological features. AI technologies, particularly through machine learning and deep learning algorithms, effectively enable noise suppression, feature extraction, and pattern recognition from high-dimensional complex data, thereby achieving precise physiological state identification and disease risk prediction. The deep integration of these two fields constructs an intelligent theranostic system spanning from "sensing-analysis-decision-making-execution," demonstrating remarkable advantages in real-time health monitoring, on-demand intelligent wound management, and synergistic tumor therapy. Although challenges persist regarding long-term material stability, data standardization, clinical trustworthiness of algorithms, and regulatory pathways, deep collaboration among materials science, AI, and clinical medicine will continue advancing this field toward truly precise and dynamic healthcare.