Published Paper Details:

The integration of artificial intelligence (AI) with optical biosensors has been an introduction to a new era in biomedical diagnostics, offering potential capabilities for the early detection of cancer, and precision and accurate retinal imaging. This review critically examines the transformative blend between AI algorithms and optical biosensing technologies, including surface plasmon resonance (SPR), fluorescence spectroscopy, and optical coherence tomography (OCT). These systems provide non-invasive, high-sensitivity diagnostic platforms capable of detecting subtle biomolecular interactions and imaging minute cellular structures, crucial for addressing the global burden of cancer and vision-related diseases. AI-powered optical biosensors enhance advanced computational techniques such as deep learning, convolutional neural networks (CNNs), and generative adversarial networks (GANs) to process complex datasets, optimize signal interpretation, and enhance diagnostic accuracy. SPR, known for its precision in detecting biomarker interactions, faces challenges like noise interference and high operational costs, which AI addresses by improving signal-to-noise ratios and automating data analysis. Similarly, fluorescence-based biosensors benefit from AI-driven algorithms to counteract limitations such as photobleaching, spectral overlap, and false positives. In retinal imaging, OCT achieves remarkable resolution, yet struggles with artifacts and restricted visualization scopes, which AI alleviates through real-time motion correction, enhanced depth resolution, and predictive imaging techniques. This review also compares the relative strengths and limitations of various AI-augmented optical biosensors, emphasizing their practical applications in clinical and research settings. Key challenges include the need for standardized datasets, the ethical implications of AI deployment, and barriers to widespread adoption due to cost and infrastructure requirements. Future innovations are anticipated to include hybrid biosensor platforms that integrate multiplexed detection capabilities, AI-enhanced multimodal imaging, and adaptive learning models capable of personalizing diagnostic outputs to individual patient profiles. In conclusion, AI-empowered optical biosensors represent a paradigm shift in diagnostic science, combining the precision of optics with the predictive power of AI to redefine early disease detection and management. Addressing current limitations through interdisciplinary collaboration and technological advancement will be pivotal in achieving scalable, cost-effective, and universally accessible solutions, thereby transforming healthcare delivery on a global scale.