On-Chip Biomimetic Intelligence: Integrating Adaptive Molecular Sensing With Longitudinal Proteomic Profiling for Advanced Disease Diagnostics

Biological, molecular, and functional states evolve across multiple timescales, limiting the utility of conventional diagnostics based on isolated measurements. This Perspective introduces on-chip biomimetic intelligence as an integrated approach combining biomimetic molecular recognition and signal transduction with dynamic sensing, adaptive local computation, and longitudinal interpretation of molecular changes. It focuses on multiplexed molecular-affinity measurements of selected disease-associated protein panels, coupled with event-driven signal processing to reduce redundant data generation. Where appropriate, sensing outputs may inform patient-specific digital twin models and dynamically update PBPK or PK-PD models. Rather than treating intelligent biosensors, neuromorphic biosensing, edge AI, and digital twins as isolated technologies, it integrates their functions and describes architectural and functional requirements for chip-level biomimetic intelligence. Implementation requires regenerable molecular-recognition interfaces, reliable molecular-to-signal transduction, and computational methods capable of estimating protein states and temporal trajectories. Causal validation and constrained closed-loop control may support clinically relevant decision-making when sufficient evidence is available. However, complete architecture has not yet been demonstrated as a single clinically validated system. Its realization requires advances in sensing stability, biocompatibility, antifouling performance, calibration, drift control, and clinical validation. Thus, biosensing shifts from static biomarker detection to longitudinal, adaptive, and clinically interpretable monitoring of changes in biological state.