Toward data-driven reliability in hydrogel semiconductor devices: forecasting approaches for degradation and signal stability

Semiconducting devices based on hydrogel are increasingly popular in wearable and flexible bioelectronics because of their biocompatibility, mechanical conformability and ionic-electronic signal transduction properties. In material classes conductive polymer hydrogels, MXene and carbon-nanomaterial composites, liquid-metal hybrids, and adhesive fibrous interfaces practical applications as wearable sensors with continuous, long-duration functionality are limited by a variety of, mechanistically coupled degradation modes: environmental instability, electrode corrosion, signal interference, mechanical fatigue and cyclic swelling-shrinking, biofouling and microbial contamination, and polymer aging. Although materials-level interventions and, individually, machine-learning approaches to sensing functionality (pattern recognition, material property prediction) have been previously reviewed, no previous literature has mapped data-driven reliability and degradation-prognostication strategies based on battery health management, renewable energy control, wearable motion-artifact rejection, structural corrosion monitoring, and additive manufacturing quality control to the specific failure modes of hydrogel semiconductor devices. This Review seals that gap, critically comparing techniques on computational cost, data requirements, and cross-domain transfer assumptions; proposing a conceptual monitoring framework with a corresponding practical implementation pathway, edge-computing considerations, and uncertainty/explainability treatment; suggesting a standardized dataset structure; and reviewing regulatory and clinical-validation pathways. This Review seeks to offer a practically actionable roadmap to validated, self-monitoring hydrogel bioelectronic systems by connecting materials science, data-driven reliability engineering and biomedical device regulation.