AI has shown promise in matching or even surpassing ophthalmologists in diagnosing corneal infections. The cornea, being the eye's outermost layer, is prone to infections that can lead to vision loss if not treated promptly. Early and accurate diagnosis is essential for effective treatment, traditionally relying on the expertise of ophthalmologists who examine symptoms and perform tests like corneal cultures and slit-lamp microscopy. Recent advancements in AI, particularly deep learning, have enabled systems to analyze corneal images, such as slit-lamp photographs or confocal microscopy scans, with remarkable accuracy. These AI models are trained using large datasets to recognize patterns and detect subtle signs of infection, distinguishing between different types of corneal pathogens (e.g., bacteria, fungi, viruses, and parasites). Some studies show AI achieving diagnostic accuracy comparable to or even better than experienced ophthalmologists, particularly in complex or ambiguous cases. The benefits extend beyond just diagnosis. AI can facilitate triage in emergency settings, prioritize cases based on severity, and even suggest tailored treatment plans. It also holds potential for use in remote or underserved areas where access to ophthalmologists may be limited. While AI shows great promise, integrating it into clinical practice raises challenges, such as ensuring the quality of data used for training, addressing biases, and meeting regulatory requirements. The role of AI is complementary rather than replacing ophthalmologists, assisting them in making quicker and more accurate decisions.