Anterior Segment and Corneal Disorders

<jats:p>Artificial intelligence (AI) has emerged as a transformative technology in anterior segment ophthalmology, leveraging high-resolution imaging to enhance clinical decision-making. This chapter synthesizes evidence on AI applications across infectious keratitis, keratoplasty, keratoconus, refractive surgery, phakic intraocular lens implantation, cataract management, and dry eye disease assessment. The evolution from traditional machine learning to deep learning (DL) represents a paradigm shift toward automated feature extraction and multimodal data integration. DL models achieve diagnostic accuracies exceeding 95% in keratoconus and fungal keratitis identification. In infectious keratitis, AI differentiates etiologies with ROC–AUC values exceeding 0.96, enabling rapid pathogen-specific diagnosis. In refractive surgery, neural networks optimize candidate selection and nomogram precision. Integrating corneal biomechanics with tomographic data through hybrid algorithms establishes new clinical standards for ectasia risk stratification. Furthermore, AI-driven models identify subclinical keratoconus progression 11.1 months earlier than traditional keratometric criteria, enabling personalized cross-linking indications. In cataract surgery and keratoplasty, AI enhances objective lens grading, intraocular lens power calculations, graft survival prediction, and intraoperative navigation. Despite these advances, barriers to adoption persist, including external validation gaps, platform interoperability challenges, and the need for explainable AI. Future directions highlight generative AI for decision support, federated learning for privacy-preserving model development, and oculomics for precision medicine. This review demonstrates AI’s potential to augment clinician expertise, reduce diagnostic variability, and improve patient outcomes, particularly in resource-limited settings</jats:p>