Self-adaptive nanophotonic metasurfaces enabling digital twins: toward continuous personalized healthcare in aging and disability.

Continuous physiological monitoring is a prerequisite in medicine that enables early disease detection, long-term health monitoring, and individualized therapeutic interventions. Although wearable optical biosensors have evolved significantly, their long-term clinical deployment remains limited by mechanical degradation, signal drift, biofouling, energy constraints, and poor integration with intelligent healthcare infrastructure. These issues are especially acute in the aged and disabled, when constant, dependable, and tailored monitoring is critical for proactive disease treatment. As a result, next-generation sensing technologies must progress beyond passive data collection toward self-adaptive systems capable of maintaining analytical accuracy while supporting patient-specific clinical interpretation and clinician-supervised decision-making. This perspective presents an overview of self-adaptive nanophotonic metasurfaces as the sensing foundation for Living Digital Twins (LDTs) in personalized healthcare. To create an intelligent sensing ecosystem, the proposed framework combines flexible plasmonic, localized surface plasmon resonance (LSPR), surface-enhanced Raman scattering (SERS), artificial intelligence, internet of nano things (IoNT) connectivity, and individualized digital twin analytics. The proposed framework combines continuous multimodal biomarker sensing with patient-specific physiological modelling to support longitudinal updating of digital representations. Edge intelligence enables low-latency signal processing, while adaptive analytics may improve sensing stability, calibration, and patient-specific interpretation. The integration of adaptive nanophotonic sensing with digital twins therefore represents a prospective approach for supporting early identification of physiological deterioration, individualized risk assessment, and clinician-supervised treatment planning in aging and disabled populations.