Uniting Minds: Global Collaborations Accelerating AI in Ophthalmology

Global researchers are advancing ophthalmic diagnostics through AI collaborations while navigating a core tension: building models that generalize across diverse settings without compromising local performance or privacy.

The RETFound initiative has been highlighted as a retina-focused foundation model trained on large-scale ophthalmic imaging, aiming to improve generalizability across diverse dataset.

Multicenter evaluations have assessed retina-focused AI on external datasets for tasks such as diabetic retinopathy screening, often reporting strong discrimination (e.g., high AUROC) versus baseline models; however, these findings should be interpreted as promising rather than as practice-changing standards.

These collaborative efforts have laid the groundwork for more reliable AI tools in retinal imaging, particularly for conditions detectable on fundus photography or OCT, such as diabetic retinopathy and age-related macular degeneration.

These collaborative results are reshaping how clinicians consider AI in real-world ophthalmic challenges. In practice, change is gradual and anchored by validated tools—for example, autonomous diabetic retinopathy screening systems cleared for use in primary care settings—and by professional-society guidance on responsible deployment, which together inform how clinics integrate AI into workflows.

For patients receiving AI-enhanced diagnostics, these tools can surface image features associated with disease risk that may be less apparent on routine review, though model performance can vary by device and population and results require clinician oversight.

Addressing misdiagnosis through AI remains crucial, particularly for retinal imaging–detectable diseases where standardized fundus or OCT data can support consistent triage.

These advancements may enable earlier detection and timelier referral in screening programs, supporting more prompt evaluation and treatment when indicated.

Key Takeaways:

• Global collaborations in AI are critical for enhancing diagnostic accuracy and ensuring broader applicability of ophthalmic models.
• RETFound has reported improvements on external validation tasks for diabetic retinopathy classification relative to baseline models, based on summary coverage.
• AI-guided workflows are being integrated cautiously alongside validated tools and professional guidance.
• Due to AI-driven insights, clinicians can support more individualized decision-making—for example, using OCT-derived risk signals to adjust follow-up intervals—while maintaining oversight.