Artificial Intelligence-Assisted Nanosensors for Clinical Diagnostics: Current Advances and Future Prospects
Shuo Yin
Abstract
The integration of artificial intelligence (AI) with various diagnostic nanosensors has opened up new horizons in clinics recently. AI technology offers enhanced sensitivity, accuracy, specificity, and real-time analysis for disease diagnostics. This review focuses on the recent advances in AI-assisted nanosensors for the diagnosis of different diseases in clinical applications. Critical roles of AI in sensor design, optimization, signal processing, and clinical decision support are highlighted. Furthermore, challenges such as limited datasets, regulatory hurdles, and data privacy are discussed, along with future opportunities. This review aims to provide a comprehensive introduction and perspectives on how AI-driven nanosensors are transforming clinical diagnostics and shaping the future of precise medicine.