Beyond the Barrier: Overcoming Ocular Antimicrobial Resistance Through AI and Novel Therapeutics

Ocular infections are a major cause of morbidity and vision loss worldwide, significantly affecting the quality of life and clinical outcomes. The management of ocular infections has become increasingly difficult due to the rising prevalence of antimicrobial resistance among commonly implicated pathogens. This review summarizes the epidemiology and etiology of ocular infections, with emphasis on bacterial pathogens frequently associated with resistance. Various mechanisms of antimicrobial resistance, including genetic mutations, intrinsic resistance, and biofilm formation, are also discussed. The review further examines the limitations of current therapeutics, such as poor ocular drug penetration, frequent dosing requirements, adverse effects, and reduced efficacy against multi-resistant organisms. In response to these challenges, the need for novel therapeutic approaches with improved stability and prolonged ocular retention is highlighted. Furthermore, the integration of artificial intelligence in ophthalmology is explored, particularly in disease diagnosis, image analysis, treatment planning, and antimicrobial resistance surveillance. Despite these advances, several translational challenges remain, including data set bias, limited external validation, regulatory approval hurdles, data privacy concerns, and restricted accessibility in resource-limited settings, which currently limit the widespread clinical implementation. Therefore, continued surveillance, rational antimicrobial use, and effective therapeutic strategies are essential to reduce the burden of ocular infections and improve patient outcomes.