[Advancements of artificial intelligence in dry eye]

Area of Science:

• Ophthalmology
• Medical Artificial Intelligence (AI)

Background:

• Dry eye disease is a prevalent ocular surface disorder with increasing global incidence.
• Complex multifactorial etiology makes dry eye diagnosis challenging.
• Ophthalmic AI presents unique advantages for diagnosing and treating eye conditions due to the eye's specific anatomy.

Purpose of the Study:

• To systematically review the applications of AI in the field of dry eye.
• To analyze the challenges and prospects of AI in the clinical diagnosis of dry eye.
• To provide guidance for the development and implementation of AI in dry eye management.

Main Methods:

• Systematic literature review of AI applications in dry eye diagnosis.
• Analysis of current AI technologies and their relevance to ophthalmic diseases.
• Discussion of challenges and future potential of AI in clinical practice.

Main Results:

• AI demonstrates significant potential in improving the accuracy and objectivity of dry eye diagnosis.
• Various AI algorithms are being explored for tasks such as image analysis and patient data interpretation.
• Challenges include data heterogeneity, algorithm validation, and clinical integration.

Conclusions:

• AI is poised to revolutionize dry eye diagnosis and management.
• Further research and development are needed to overcome existing challenges and realize AI's full potential in ophthalmology.
• AI tools can enhance clinical decision-making and improve patient outcomes for dry eye disease.