Next-generation analytical platforms for neurochemical sensing: from conventional biosensors to intelligent brain interfaces

Neurochemical sensing is central to understanding brain function, neurological disorders, and precision neurotherapeutics. Despite advances in electrochemical, optical, implantable, wearable, and genetically encoded technologies, current platforms remain limited by molecular selectivity, long-term stability, biofouling, complex data interpretation, and clinical translation. This review critically examines 83 representative studies encompassing nanomaterial-enabled electrochemical sensors, implantable and wearable devices, genetically encoded and CRISPR-engineered systems, AI-assisted analytical frameworks, and multimodal bioelectronic interfaces. Among the reviewed platforms, 68.7% were based on electrochemical or electrical sensing, whereas only 9.6% incorporated explicit AI/ML-assisted data analysis, indicating that intelligent analytical functionalities remain relatively limited. AI-assisted platforms have shown promising capabilities for signal discrimination, quantitative analysis, and enhanced analytical performance across diverse neurochemical sensing modalities, but their reported performance remains highly dependent on the sensing architecture, experimental conditions, and level of validation. Rather than providing a conventional technology-centered survey, this review establishes a unified framework for evaluating neurochemical sensing across five dimensions: molecular selectivity, spatiotemporal resolution, operational stability, intelligent data interpretation, and translational readiness. The reviewed evidence indicates a transition from conventional single-modality sensing toward integrated platforms incorporating multimodal detection and intelligent data interpretation, with adaptive and closed-loop interfacing emerging as future directions. By connecting advances in molecular recognition, functional materials, bioelectronics, and AI, this review identifies key design principles, persistent bottlenecks, and realistic priorities for translating next-generation neurochemical sensing platforms into reliable and clinically relevant intelligent interfaces.