The Intelligence Revolution in Biosensing: Transforming Raw Data into Smart Clinical Diagnostics

Artificial intelligence (AI) is revolutionizing nanobiotechnology-enabled biosensing by combining advanced nanomaterials with intelligent data analytics to create next-generation diagnostic platforms. This review summarizes recent progress in AI-integrated nano-biosensors, highlighting the contributions of functional nanomaterials such as graphene, carbon nanotubes, metal oxides, quantum dots, and hybrid nanocomposites in enhancing sensitivity, selectivity, and signal transduction. Machine learning and deep learning techniques, including support vector machines, random forests, convolutional neural networks, and transformer-based models, are examined for their roles in feature extraction, noise reduction, and multi-analyte prediction from complex biological samples. The synergy between nanomaterial properties and AI-driven optimization has facilitated the development of real-time, miniaturized, and wearable diagnostic devices. Applications in cancer, metabolic, infectious, and neurological disease diagnostics are critically reviewed, demonstrating improved analytical performance, ultralow detection limits, and enhanced diagnostic accuracy. Emerging technologies such as edge AI, federated learning, explainable AI, and self-powered nanosystems are also discussed for their potential in decentralized and personalized healthcare. Challenges related to data quality, nanomaterial reproducibility, scalability, and regulatory approval remain significant barriers to clinical translation. Nevertheless, the integration of AI and nanobiotechnology offers a powerful framework for developing intelligent biosensing systems and advancing modern analytical and clinical diagnostics.