Breaking Lab Barriers: Artificial Intelligence for Clinical Translation of Optical Sensors in Healthcare

Abstract

Background/Objectives: The growing demand for precision prevention and personalized medicine has accelerated the need for non/minimally invasive, point-of-care testing (POCT), and continuous monitoring systems. Optical sensors offer significant advantages for such applications, including high sensitivity, rapid response, and excellent immunity to electromagnetic interference. However, their translation into clinical practice is hindered by issues such as low signal-to-noise ratios and complex data structures. Artificial intelligence (AI) can help overcome these limitations by improving weak signal extraction, high-dimensional signal analysis, and multimodal data integration. Accordingly, this review aims to examine how AI can help optical sensors overcome these challenges and to identify the major barriers limiting their adoption in clinical settings. Methods: We first review four key classes of optical hardware: optical fiber sensors, integrated nanophotonic devices, laser/spectral imaging systems, and wearable flexible devices. Building upon these platforms, we further evaluate common data processing methodologies, highlighting their effectiveness in weak-signal amplification, feature extraction, and cross-source data fusion. Results: Across these platforms, data-driven methods have improved signal recovery, noise suppression, and drift compensation and have also enabled the extraction of clinically useful features from high-dimensional data. Multimodal integration may further reduce diagnostic error. Early clinical use has emerged in home monitoring, intraoperative guidance, and POCT. However, translation remains constrained by sample complexity, manufacturing variability, limited long-term stability, and the mismatch between rapid algorithm updates and conventional regulatory pathways. Conclusions: Optical sensing is moving from passive measurement toward more integrated diagnostic workflows. Further progress will depend on closed-loop sensing-computing-intervention systems, edge deployment, adaptive regulation, scalable manufacturing, and improved long-term robustness. Coordinated progress across technology, clinical validation, industry, and regulation will be essential for broader clinical adoption.