Purpose of reviewThe application of artificial intelligence (AI) in medicine and ophthalmology has experienced exponential breakthroughs in recent years in diagnosis, prognosis, and aiding clinical decision-making. The use of digital data has also heralded the need for privacy-preserving technology to protect patient confidentiality and to guard against threats such as adversarial attacks. Hence, this review aims to outline novel AI-based systems for ophthalmology use, privacy-preserving measures, potential challenges, and future directions of each. Recent findingsSeveral key AI algorithms used to improve disease detection and outcomes include: Data-driven, imagedriven, natural language processing (NLP)-driven, genomics-driven, and multimodality algorithms. However, deep learning systems are susceptible to adversarial attacks, and use of data for training models is associated with privacy concerns. Several data protection methods address these concerns in the form of blockchain technology, federated learning, and generative adversarial networks. SummaryAI-applications have vast potential to meet many eyecare needs, consequently reducing burden on scarce healthcare resources. A pertinent challenge would be to maintain data privacy and confidentiality while supporting AI endeavors, where data protection methods would need to rapidly evolve with AI technology needs. Ultimately, for AI to succeed in medicine and ophthalmology, a balance would need to be found between innovation and privacy.

Ocular surface diseases (OSDs) are significant causes of ocular morbidity, and are often associated with chronic inflammation, redness, irritation, discomfort, and pain. In severe OSDs, loss of vision can result from ocular surface failure, characterised by limbal stem cell deficiencies, corneal vascularisation, corneal opacification, and surface keratinisation. External and internal exposomes are measures of environmental factors that individuals are exposed to, and have been increasingly studied for their impact on ocular surface diseases. External exposomes consist of external environmental factors such as dust, pollution, and stress; internal exposomes consist of the surface microbiome, gut microflora, and oxidative stress. Concerning internal exposomes, alterations in the commensal ocular surface microbiome of patients with OSDs are increasingly reported due to advancements in metagenomics using next-generation sequencing. Changes in the microbiome may be a consequence of the underlying disease processes or may have a role in the pathogenesis of OSDs. Understanding the changes in the ocular surface microbiome and the impact of various other exposomes may also help to establish the causative factors underlying ocular surface inflammation and scarring, the hallmarks of OSDs. This review provides a summary of the current evidence on exposomes in various OSDs.