This study explores the potential of Artificial Intelligence (AI) in early screening and prognosis of Dry Eye Disease (DED), aiming to enhance the accuracy of therapeutic approaches for eye-care practitioners. Despite the promising opportunities, challenges such as diverse diagnostic evidence, complex etiology, and interdisciplinary knowledge integration impede the interpretability, reliability, and applicability of AI-based DED detection methods. The research conducts a comprehensive review of datasets, diagnostic evidence, and standards, as well as advanced algorithms in AI-based DED detection over the past five years. The DED diagnostic methods are categorized into three groups based on their relationship with AI techniques: (1) those with ground truth and/or comparable standards, (2) potential AI-based methods with significant advantages, and (3) supplementary methods for AI-based DED detection. The study proposes suggested DED detection standards, the combination of multiple diagnostic evidence, and future research directions to guide further investigations. Ultimately, the research contributes to the advancement of ophthalmic disease detection by providing insights into knowledge foundations, advanced methods, challenges, and potential future perspectives, emphasizing the significant role of AI in both academic and practical aspects of ophthalmology.

Constructing graphene-based heterostructures with large interfacial area is an efficient approach to enhance the electrochemical performance of supercapacitors but remains great challenges in their synthesis. Herein, a novel ultra-small amorphous Fe₂O₃ nanodots/graphene heterostructure (a-Fe₂O₃ NDs/RGO) aerogel was facilely synthesized via excessive metal-ion-induced self-assembly and subsequent calcination route using Prussian blue/graphene oxide (PB/GO) composite aerogel as precursors. The deliberately designed a-Fe₂O₃ NDs/RGO heterostructure offers a highly interconnected porous conductive network, large heterostructure interfacial area, and plenty of accessible active sites, greatly facilitating the electron transfer, electrolyte diffusion, and pseudocapacitive reactions. The obtained a-Fe₂O₃ NDs/RGO aerogel could be used as flexible free-standing electrodes after mechanical compression, which exhibited a significantly enhanced specific capacitance of 347.4 F·g⁻¹ at 1 A·g⁻¹, extraordinary rate capability of 184 F·g⁻¹ at 10 A·g⁻¹, and decent cycling stability. With the as-prepared a-Fe₂O₃ NDs/RGO as negative electrodes and the Co₃O₄ NDs/RGO as positive electrodes, an all-solid-state asymmetric supercapacitor (a-Fe₂O₃ NDs/RGO//Co₃O₄ NDs/RGO asymmetric supercapacitor (ASC)) was assembled, which delivered a high specific capacitance of 69.1 F·g⁻¹ at 1 A·g⁻¹ and an impressive energy density of 21.6 W·h·kg⁻¹ at 750 W·kg⁻¹, as well as good cycling stability with a capacity retention of 94.3% after 5,000 cycles. This work provides a promising avenue to design high-performance graphene-based composite electrodes and profound inspiration for developing advanced flexible energy-storage devices.