Artificial Intelligence in the Detection of Papilledema: A Systematic Review

Background/Objectives: This review explores the role of artificial intelligence (AI), particularly with deep learning and machine learning, in the detection and classification of papilledema using retinal fundus imaging. Methods: The study synthesizes historical, technical, and clinical insights, comparing AI-based diagnostic accuracy with conventional methods. Results: Our findings demonstrate that AI systems, especially convolutional neural networks (CNNs), offer sensitivity and specificity comparable to, or even surpassing, expert-level fundoscopy. Conclusions: These results suggest significant implications for early diagnosis, triage, and telemedicine integration in ophthalmic care.