Next‐Generation Electrochemical Analytical Systems: A Critical Review of Biosensing Technologies, Miniaturized Platforms, and Future Intelligent Diagnostics

Electrochemical analytical systems have evolved from conventional sensing devices into advanced platforms capable of real-time monitoring, multiplexed analysis, and decentralized diagnostics. Progress in biosensing technologies, nanomaterials, microfluidics, wearable devices, and artificial intelligence has significantly improved analytical performance and expanded potential applications in healthcare, environmental monitoring, and food safety. This review critically evaluates recent advances in next-generation electrochemical analytical systems, focusing on biosensing technologies, nanomaterial-enabled platforms, miniaturized devices, precision diagnostics, and emerging intelligent analytical systems, and identifies the major barriers to their practical implementation. Advanced electrochemical biosensors, including enzymatic, immunosensors, aptasensors, nucleic acid sensors, and molecularly imprinted polymer-based platforms, have demonstrated remarkable sensitivity and selectivity. Nanomaterials such as graphene, carbon nanotubes (CNTs), MXenes, and metal nanoparticles have further enhanced signal amplification and electron-transfer efficiency. In parallel, miniaturized technologies, including screen-printed electrodes (SPE), paper-based devices, microfluidic systems, and wearable sensors, have accelerated point-of-care diagnostics. However, persistent challenges related to matrix effects, biofouling, reproducibility, standardization, long-term stability, clinical validation, and manufacturing scalability continue to hinder widespread translation. The future of electrochemical analytical systems lies in developing robust, standardized, and clinically validated intelligent diagnostic ecosystems that integrate advanced sensing technologies with artificial intelligence, digital connectivity, and automated data analysis. Achieving successful clinical translation will require greater emphasis on reproducibility, regulatory compliance, multicenter validation, and real-world applicability, rather than solely on achieving ever-lower detection limits.