Machine learning-empowered electrochemical nanobiosensors: towards intelligent diagnostics and data analysis.
TL;DR
The central conclusion is that computational sophistication cannot repair absent chemical selectivity or non-independent validation: credible intelligent sensing requires mechanism-grounded signals, information-appropriate models, and validation at the level of the intended deployment claim.
Abstract
Electrochemical nanobiosensors now produce not only scalar analytical readouts but also voltammetric waveforms, impedance spectra, transistor transfer curves, images, and longitudinal streams whose interpretation is affected by matrix interference, fouling, drift, fabrication variability, and population heterogeneity. This Review introduces a mechanism-information-validation (MIV) framework for evaluating when machine learning (ML) changes the scientific or translational value of those signals. The mechanism layer asks how recognition chemistry, double-layer structure, receptor density, linker chemistry, antifouling design, and electron-transfer kinetics generate or distort the measured response. The information layer matches the signal object-scalar, curve, spectrum, image, graph, or time series-to chemometrics, conventional ML, deep learning, or constrained models. The validation layer identifies the independent unit represented by a split-scan, electrode, batch, specimen, participant, or external centre-and asks whether the evidence supports the stated analytical or clinical claim. Using this framework, we compare amperometric, voltammetric, impedimetric, potentiometric and field-effect transistor platforms; incorporate aptamer-FET and continuous reagentless electrochemical aptamer-based sensing; and examine computational electroanalysis beyond post hoc classification, including multivariate fast-scan cyclic voltammetry, waveform design, graph neural networks, physics-informed neural networks, and foundation-model-assisted analysis. Rebuilt benchmarking tables separate analytical sensitivity from diagnostic evidence, model validation, and translational readiness. The final section connects representative data, subgroup performance, model locking, change control, external validation, and post-deployment monitoring to current Good Machine Learning Practice. The central conclusion is that computational sophistication cannot repair absent chemical selectivity or non-independent validation: credible intelligent sensing requires mechanism-grounded signals, information-appropriate models, and validation at the level of the intended deployment claim.
