Hydrogel-Based Colorimetric Biosensors Enhanced by Artificial Intelligence for Wearable Diagnostics

Recent advances in hydrogel-based colorimetric biosensors have enabled new pathways for non-invasive and real-time health monitoring, particularly when integrated with wearable formats. These platforms exploit the optical responsiveness of embedded materials to transduce biochemical events into visible signals, supporting point-of-care diagnostics without the need for complex electronics. Hydrogels serve as versatile scaffolds, offering high water retention, biocompatibility, and tunable porosity that facilitate fluid absorption, analyte transport, and biorecognition. However, challenges such as signal variability, fluid volume dependence, and environmental interferences have hindered their clinical translation. The convergence of materials science with artificial intelligence (AI) presents a promising strategy to overcome these limitations. Machine learning (ML) and deep learning (DL) algorithms enhance signal interpretation by compensating for non-linearities, correcting ambient interferences, and enabling data-driven calibration across user-specific variables. This chapter provides an overview of recent progress in hydrogel-based wearable colorimetric sensors, with particular emphasis on materials design, sensing mechanisms, and AI-assisted signal processing. Representative case studies demonstrate how smart material integration and computational tools can synergistically improve sensitivity, accuracy, and reliability in decentralized diagnostics.