Artificial intelligence in retinal disease: clinical application, challenges, and future directions

Area of Science:

• Ophthalmology
• Medical Imaging
• Artificial Intelligence

Background:

• Retinal diseases are a primary cause of blindness globally, affecting all age groups.
• Current diagnostic and treatment pathways require specialized clinicians, leading to potential delays.
• An aging population and increasing disease prevalence highlight the need for efficient healthcare solutions.

Purpose of the Study:

• To review the current research on AI applications for retinal disease management.
• To explore AI's role in decision support for diagnosis, classification, monitoring, and treatment.
• To focus on key conditions: diabetic retinopathy, age-related macular degeneration, inherited retinal disease, and retinopathy of prematurity.

Main Methods:

• Systematic review of AI research in retinal disease.
• Prioritization of specific retinal conditions for analysis.
• Evaluation of AI's potential for clinical integration.

Main Results:

• AI algorithms show promise in processing multimodal retinal imaging data.
• AI offers decision support for diagnosing and monitoring major retinal diseases.
• Research indicates AI can enhance efficiency and clinician support.

Conclusions:

• AI holds potential to expedite diagnosis and treatment of retinal diseases.
• Integration of AI can improve healthcare system efficiency and patient access to care.
• AI can assist clinicians in managing complex data, allowing more time for patient interaction.