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The theory of critical slowing down (CSD) posits that as a complex system approaches a critical tipping point, its ability to recover from perturbations significantly declines due to a loss of resilience. Consequently, CSD serves as a potent early warning signal for imminent state transitions. As an extension of CSD applied to high-dimensional systems, dynamic network biomarkers (DNBs) have demonstrated clinical value in providing early warnings for diseases such as cancer. With advancements in artificial intelligence and multi-omics technologies, numerous ocular disease-related biomarkers have been identified. However, current clinical applications primarily utilize these biomarkers to assess disease severity or predict incidence probabilities, failing to provide early warnings of imminent critical transitions within the crucial reversible therapeutic window. Integrating these newly discovered biomarkers with CSD and DNB theories represents a promising entry point for achieving precision diagnosis and treatment in ophthalmology. This article systematically elucidates the conceptual frameworks, underlying principles, and historical evolution of CSD/DNB theories, exploring their potential for cross-disciplinary synergy within the ophthalmic field. Addressing the clinical bottleneck of high-frequency longitudinal data acquisition, we propose technical pathways including single-sample network inference and home-based monitoring strategies. This review primarily investigates the potential of these theories in monitoring homeostatic stability in neovascular diseases, assessing progression risks in neurodegenerative conditions such as glaucoma, and predicting structural decompensation in myopia and corneal ectasia. Furthermore, by contrasting CSD/DNB with traditional artificial intelligence-based predictive models, this work provides a novel theoretical perspective for the proactive monitoring and early intervention of ocular diseases.