Retinal diseases such as cataract, diabetic retinopathy, and glaucoma affect millions across Asia. Recognizing the challenges of delayed diagnosis and limited access to expert care, one of Asia’s largest ophthalmology clinics partnered with GoML to deploy an AI-powered retinal screening system. This cutting-edge solution, powered by CNN-based models and optimized for real-world clinical use, demonstrates how AI for retinal disease detection is reshaping early diagnosis, reducing costs, and improving access to vision-saving interventions.

The problem: delayed diagnoses and inaccessible screening

Across India, manual screening for retinal diseases is expensive, time-intensive, and highly dependent on specialist availability. For this clinic, a surge in outpatient visits and limited expert capacity meant that over 35% of early-stage retinal issues went undetected, especially in remote satellite centers.

The lack of real-time tools, costly hardware, and the need for expert interpretation created a bottleneck. Moreover, traditional ML models faced challenges like dataset imbalance, overfitting, and inefficiency at scale, rendering them impractical for hospital-grade deployment.

For the clinic, better AI for retinal disease detection wasn’t a nice-to-have; it was a strategic imperative to deliver early detection at scale without compromising diagnostic accuracy.

The solution: AI for retinal disease detection at speed and scale

To tackle these barriers, the clinic deployed a two-tiered AI solution: SmallRetinaNet for fast screening at satellite centers, and RetinaNet for high-capacity analysis at hospital hubs. Together, they enabled a seamless AI workflow integrated into hospital systems, unlocking real-time disease detection through AI for retinal disease detection in clinical networks.

Complete diagnostic support anywhere

• SmallRetinaNet enables high-speed inference even on mobile and low-resource deployments and perfect for rural and satellite centers.

• RetinaNet, deployed via AWS SageMaker, powers deeper analysis in central hospitals, aiding ophthalmologists with precision insights.

Fast, smart classification

• The models classify images into four disease categories: cataract, diabetic retinopathy, glaucoma, and normal retina within seconds, enabling instant triaging and targeted care.

Built for scale and performance

• AWS Lambda and API Gateway ensure fast, secure processing.

• Images are stored in S3 and predictions are saved in DynamoDB.

• Seamless integration with hospital applications via Flask APIs.

Clear results where it matters

• Real-time results delivered via mobile/web app to clinicians and patients.

• Integrated authentication with AWS Cognito ensures secure access for healthcare professionals.

• Dashboard analytics enable hospital admins to monitor population-level trends.

The impact: smarter screening, faster referrals, and broader reach

GoML’s AI for retinal disease detection deployment is helping the clinic reimagine vision care at scale. With real-time, automated screening available at both primary and tertiary levels, the system enables more proactive, timely interventions, demonstrating the power of AI  for population-level impact.

• 90% improved diagnostic accessibility with SmallRetinaNet deployed in rural outreach centers

• 70% increase in diagnostic accuracy, minimizing false negatives and missed cases

• 85% faster patient triage, reducing delays in specialist referrals

Lessons for other healthcare providers

What the clinic learned from building Gen AI-powered diagnostic workflows:

Common pitfalls to avoid

• Using a one-size-fits-all AI model for diverse clinical needs

• Ignoring infrastructure requirements for rural and mobile deployment

• Treating AI as a research tool rather than a care enabler

Advice for clinical teams facing similar challenges

• Design multi-tier AI systems: lightweight for scale, powerful for depth

• Start with a focused use case, like diabetic retinopathy screening, for quick adoption

• Partner with tech teams that understand hospital workflows, not just AI

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Want to screen more patients, more accurately, with fewer resources?

Let GoML help you bring AI for retinal disease detection to the heart of clinical care, just like we did with Asia’s second-largest ophthalmology clinic.