Retinal imaging and artificial intelligence analysis

The retina, uniquely accessible as an extension of the central nervous system, plays a critical role in neurology and ophthalmology. Advances in retinal imaging technologies - including color fundus photography, optical coherence tomography, and OCT angiography - have enabled detailed, noninvasive assessment of ocular and systemic diseases. Artificial intelligence (AI), particularly deep learning, has revolutionized the analysis of these images, facilitating early detection, classification, and prognostication of conditions such as diabetic retinopathy, age-related macular degeneration, optic neuropathies, and inherited retinal diseases. AI applications extend beyond ophthalmology, offering insights into neurologic and cerebrovascular disorders through retinal biomarkers. However, challenges remain regarding dataset bias, generalizability, ethical concerns, and the "black box" nature of AI models. Addressing these limitations, along with integrating multimodal imaging and fostering cross-specialty collaboration, will be pivotal in ensuring that AI technology is adopted effectively and responsibly. The future will be marked by synergistic AI-human workflows to enhance diagnostic accuracy, screening scalability, and personalized patient care. As AI applications evolve, they promise to reshape retinal imaging and its intersection with neurology, expanding opportunities for early intervention and improved outcomes.