Machine learning-empowered electrochemical nanobiosensors: towards intelligent diagnostics and data analysis

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.