Comparative Evidence for Wearable Hydrogel Biosensing And Clinical Risk Prediction: Comparative Methods and Boundary Conditions

Abstract

Progress in wearable hydrogel biosensing and clinical risk prediction depends on more than accumulating favorable results. This critical synthesis connects dynamic-covalent hydrogel sensing combined with machine learning for Parkinsonian assessment and encrypted interaction with a clinical nomogram and web calculator for individualized lymph-node-metastasis risk and asks how measurement choices, boundary conditions, and decision costs shape the interpretation of both. The review draws on two focal records and 12 established sources already present in the project evidence cache. Its comparative framework links network chemistry, mechanical compliance, and signal stability to downstream questions of clinical features and privacy. The combined literature indicates that methodological gains become actionable only when network chemistry and mechanical compliance are evaluated together and when limits associated with privacy are explicit. This shifts the emphasis from isolated scores toward traceable chains of evidence and decision relevance. On this basis, the review proposes an auditable pathway from focal mechanism to application claim, with explicit checkpoints for calibration, external validity, and responsible interpretation.