This chapter examines the operational, ethical, and translational challenges of integrating artificial intelligence (AI) into ophthalmic imaging systems. While AI, particularly deep learning (DL) and convolutional neural networks (CNNs), has achieved remarkable diagnostic accuracy in controlled studies, real-world implementation faces persistent barriers related to data quality, model reliability, cybersecurity, and regulatory compliance. This chapter discusses how limited dataset diversity, inconsistent labeling, and inadequate transparency can compromise the generalizability and fairness of algorithms. Cybersecurity threats and privacy concerns further underscore the need for robust data governance frameworks, including compliance with the General Data Protection Regulation (GDPR) and similar laws. Algorithmic fragility, bias, and lack of interpretability remain major obstacles, driving interest in explainable AI (XAI) and standardized reporting tools such as the Checklist for AI in Medical Imaging (CLAIM). The discussion extends to the sociotechnical dimensions of AI adoption, highlighting clinician resistance, legal ambiguities, and the need for multidisciplinary education and stakeholder collaboration. Finally, the chapter reviews emerging regulatory frameworks from the FDA, EMA, and WHO that emphasize continuous post-market monitoring, transparency, and ethical accountability. Successfully addressing these operational and translational challenges will be essential for advancing trustworthy, equitable, and clinically effective AI deployment in ophthalmic imaging.