AI‐Integrated Optoelectronic Sensors for Real‐Time Chemical Exposure and Health Risk Assessment

Vollständiger Abstract

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ABSTRACT Background Artificial intelligence (AI)‐integrated optoelectronic sensing platforms are gaining increasing attention as next‐generation tools for monitoring chemical exposures and assessing associated human health risks in real time. Growing concerns over environmental pollutants, toxic chemicals, and continuous exposure at the environment–human interface have highlighted the need for sensitive, adaptive, and data‐driven diagnostic technologies that extend beyond conventional laboratory‐based analysis. Unlike previous review articles that typically address optoelectronic sensing technologies or AI‐based analytics separately, this work provides a unified and application‐oriented perspective by integrating sensing mechanisms, bioelectronic system design, and AI‐driven data interpretation within a single framework. Methods This review synthesizes recent advances in AI‐integrated optoelectronic sensing platforms by comparatively examining sensor architectures, transduction mechanisms, data acquisition strategies, and AI‐based analytical frameworks reported between 2020 and 2025. Relevant studies were identified through a targeted literature survey of major peer‐reviewed journals, focusing on experimental and applied research demonstrating quantifiable sensing performance, real‐time monitoring capability, or health‐relevant chemical detection. Purely theoretical studies or systems lacking bioelectronic relevance were excluded unless they provided transferable insights into signal processing or AI‐assisted interpretation. Results The reviewed studies demonstrate that AI integration substantially enhances sensitivity, selectivity, and robustness of bioelectronic sensing platforms, enabling accurate detection of low‐level chemical exposures and dynamic exposure profiling across wearable, implantable, and portable systems. Conclusions AI‐enabled optoelectronic sensing platforms represent a promising pathway toward proactive, personalized, and real‐time chemical exposure assessment, with strong potential for future applications in environmental monitoring, diagnostics, and preventive healthcare. From a translational perspective, their successful deployment will depend on large‐scale validation, standardization of sensing protocols, integration with existing healthcare and environmental monitoring infrastructures, and the development of regulatory‐compliant, scalable, and user‐friendly systems.