Solving the Data Challenge in Ophthalmic AI

At ARVO, Cecilia S. Lee, MD, MS, and Aaron Y. Lee, MD, MSCI, discuss barriers to AI deployment in ophthalmology, including interoperability and model development.

By
Cecilia S. Lee, MD, MS
Aaron Y. Lee, MD, MSCI
May 1, 2026
4 min

Ophthalmology Management

Objective:

To discuss barriers to AI deployment in ophthalmology and explore solutions for data standardization and sharing.

Key Findings:

Standardization of imaging devices is crucial for effective AI model training.
Data sharing must prioritize participant privacy to build trust.
Publicly accessible datasets can facilitate research and model training.

Interpretation:

The ophthalmology field is moving towards better data standardization and sharing practices, which are essential for advancing AI applications in clinical settings.

Limitations:

Challenges remain in ensuring data privacy while promoting data sharing.
The transition to standardized practices may take time and require collaboration with multiple stakeholders.

Conclusion:

Addressing data challenges is vital for the successful integration of AI in ophthalmology, with ongoing efforts to enhance data accessibility and trust.

Solving the Data Challenge in Ophthalmic AI

Objective:

Key Findings:

Interpretation:

Limitations:

Conclusion:

Original Source(s)

Solving the Data Challenge in Ophthalmic AI

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