Emerging trends in AI-integrated optical biosensors for point-of-care diagnostics: current status and future prospects

Abstract

Optical biosensors have emerged as a transformative class of point-of-care diagnostic (POCD) devices, offering sensitive, specific, and rapid detection of diseases. The integration of optical biosensors with artificial intelligence (AI) brings a new revolution to the field of POCD by enabling enhanced analytical performance and real-time decision-making. This review presents an overview of the existing and upcoming prospects of AI-integrated optical biosensors with an emphasis on progress in sensor design, data science, and miniaturization. We also point out the advantages of AI algorithms, especially machine learning and deep learning, in improving the sensitivity, specificity, and multiplexing of optical biosensors during intelligent signal processing, pattern recognition, and automated decision-making. The optical biosensing techniques, including SPR, fluorescence, colorimetric, and Raman-based methods, are reviewed concerning improvements facilitated by AI technology. Finally, we examine the possibilities of integrating optical biosensors with IoT and cloud computing and critically address challenges related to data privacy, integration complexity, and clinical validation. To summarize, this review provides a realistic and future-oriented outlook to researchers, clinicians, and industry stakeholders interested in using AI-enhanced optical biosensors in redefining the future of POCD.