The integration of flexible materials with optical sensing technologies has advanced wearable optical biosensors, offering significant potential in personalized medicine, health monitoring, and disease prevention. This review summarizes the recent advancements in flexible materials for wearable optical biosensors, with a focus on materials such as polymer substrates, nanostructured materials, MXenes, hydrogels, and textile-based integrated platforms. These materials enhance the functionality, sensitivity, and adaptability of sensors, particularly in wearable applications. The review also explores various optical sensing mechanisms, including surface plasmon resonance (SPR), optical fiber sensing, fluorescence sensing, chemiluminescence, and surface-enhanced Raman spectroscopy (SERS), emphasizing their role in improving the detection capabilities for biomarkers, physiological parameters, and environmental pollutants. Despite significant advancements, critical challenges remain in the fabrication and practical deployment of flexible optical biosensors, particularly regarding the long-term stability of materials under dynamic environments, maintaining reliable biocompatibility during prolonged skin contact, and minimizing signal interference caused by motion artifacts and environmental fluctuations. Addressing these issues is vital to ensure robustness and accuracy in real-world applications. Looking forward, future research should emphasize the development of multifunctional and miniaturized devices, the integration of wireless communication and intelligent data analytics, and the improvement of environmental resilience. Such innovations are expected to accelerate the transition of flexible optical biosensors from laboratory research to practical clinical and consumer healthcare applications, paving the way for intelligent health management and early disease diagnostics. Overall, flexible optical biosensors hold great promise in personalized health management, early disease diagnosis, and continuous physiological monitoring, with the potential to revolutionize the healthcare sector.

Accurate classification of lymphoma cell subtypes is essential for disease diagnosis and therapeutic decision-making, yet conventional approaches often rely on fluorescence labeling, labor-intensive sample preparation, and specialized instrumentation, limiting their applicability for rapid, label-free single-cell analysis. Here, we present an AI-assisted microfluidic light-scattering imaging platform for label-free classification of lymphoma cells. The platform integrates hydrodynamic focusing within a microfluidic chip, continuous acquisition of two-dimensional (2D) light-scattering patterns, automated image preprocessing, and transfer learning based on a pretrained ResNet50 network for intelligent optical feature extraction and classification. Human B lymphoma (Daudi) and T lymphoblastic lymphoma (SUP-T1) cells were used to evaluate the proposed framework. The optical imaging system was first validated using standard microspheres, demonstrating reliable acquisition of light-scattering patterns under continuous-flow conditions. A dataset comprising 800 single-cell scattering patterns was subsequently established and evaluated using stratified five-fold cross-validation. The proposed framework achieved an average classification accuracy of 94.75% with an average area under the receiver operating characteristic (ROC) curve of 0.986. By integrating microfluidic optical biosensing with deep learning, this work enables automated interpretation of intrinsic optical scattering signatures and provides a promising AI-enabled strategy for rapid, label-free lymphoma screening and intelligent healthcare applications.