Transformative applications of oculomics-based AI approaches in the management of systemic diseases: A systematic...

Area of Science:

• Ophthalmology
• Artificial Intelligence
• Medical Diagnostics

Background:

• Systemic diseases present significant global health challenges with high mortality rates.
• Current diagnostic methods are often complex, expensive, and invasive, hindering timely detection.
• There is a need for cost-effective, non-invasive methods for systemic disease management.

Purpose of the Study:

• To systematically review the use of artificial intelligence (AI) algorithms in managing systemic diseases.
• To analyze the potential of oculomics (ophthalmic features) for disease management.
• To assess AI's accuracy and feasibility using non-invasive ophthalmic imaging.

Main Methods:

• Systematic review of studies applying AI to ophthalmic data for systemic disease management.
• Analysis of oculomics-based AI algorithms for prediction and staging.
• Evaluation of study quality and potential biases.

Main Results:

• AI demonstrates promising accuracy in predicting systemic diseases using oculomics.
• AI shows potential for disease staging, though caution is needed regarding low-quality studies.
• Oculomics-based AI systems are cost-effective, safe, and well-accepted by patients and clinicians.

Conclusions:

• Oculomics-based AI holds significant potential for revolutionizing systemic disease management.
• AI offers a viable, non-invasive strategy for early detection, diagnosis, and monitoring.
• Further high-quality research is essential to fully realize the capabilities of AI in oculomics.