AI-Driven Advanced Techniques for Detecting Dry Eye Disease Using Multi-Source Evidence: Case Studies, Applications, Challenges, and Future Perspectives

This study examines the transformative potential of Artificial Intelligence (AI) in the early diagnosis and prognostic evaluation of Dry Eye Disease (DED), aiming to elevate the precision of clinical interventions for eye-care specialists. Despite AI’s promising capabilities, its deployment is hindered by challenges such as diverse diagnostic inputs, the multifaceted etiology of DED, and the integration of cross-disciplinary expertise, all of which affect the transparency, reliability, and practical utility of AI-driven detection systems. Through a thorough analysis of the past five years, we assess datasets, diagnostic criteria, standardized benchmarks, and cutting-edge AI algorithms central to DED detection. We organize DED diagnostic strategies into three categories based on their alignment with AI technologies: (1) methods rooted in established benchmarks or comparable standards, (2) pioneering AI techniques with distinct advantages, and (3) supportive approaches enhancing AI-based detection. This research proposes refined diagnostic protocols, promotes the synthesis of multiple evidence sources, and delineates future research directions to guide subsequent investigations. By elucidating foundational insights, innovative methodologies, persistent challenges, and prospective pathways, this work advances ophthalmic disease detection, highlighting AI’s critical role in both scholarly inquiry and clinical ophthalmology.