A Clinician's Guide to Sharing Data for AI in Ophthalmology

Area of Science:

• Ophthalmology
• Medical AI
• Data Science

Background:

• AI model performance relies heavily on data quality, quantity, and diversity.
• Ophthalmology offers rich data (imaging, records, eye-tracking) for AI applications.
• Medical data sharing faces significant privacy and regulatory hurdles.

Purpose of the Study:

• To review traditional and nontraditional data sharing methods in medicine.
• To focus on applications and considerations within ophthalmology.
• To guide researchers in ethical medical AI development.

Main Methods:

• Exploration of traditional data transfer techniques.
• Analysis of novel privacy-preserving methods (derived datasets, secure environments, model-to-data).
• Review of existing ophthalmology literature on data sharing.

Main Results:

• Traditional methods involve direct data transfer.
• Nontraditional methods prioritize security and privacy.
• Various approaches offer different advantages and disadvantages for medical data sharing.

Conclusions:

• Informed decision-making regarding data sharing is crucial for medical AI.
• Upholding ethical standards and patient privacy is paramount.
• Understanding diverse data sharing strategies enhances AI development in ophthalmology.