Abstract
Dry eye disease (DED) is a multifactorial condition affecting millions worldwide, characterized by discomfort, visual disturbance, and potential damage to the ocular surface. The complexity of its diagnosis and management, driven by the diversity of symptoms and underlying causes, presents significant challenges to clinicians. Artificial intelligence (AI) has emerged as a transformative tool in healthcare, offering potential solutions to these challenges through its data analysis, pattern recognition, and predictive modeling capabilities. This narrative review explores the role of AI in diagnosing, treating, and managing dry eye disease. AI-driven tools such as machine learning algorithms, imaging technologies, and diagnostic platforms are examined for their ability to enhance diagnostic accuracy, personalize treatment approaches, and optimize patient outcomes. Furthermore, the review addresses the limitations of AI technologies in ophthalmology, including the need for robust clinical validation, data privacy concerns, and the ethical considerations of integrating AI into clinical practice. The findings suggest that while AI holds promise for improving the care of patients with DED, ongoing research and development are crucial to realizing its full potential.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
