Financial Landscape

Diabetes costs the U.S. over $400 billion annually, with diabetic retinopathy (DR) accounting for more than $11 billion in treatment costs. An estimated 8.4 million Americans—11.6% of the population—live with diabetes, many of whom are at risk for vision loss due to DR. At the same time, access to eye care is eroding: demand is projected to rise 24%, while the ophthalmology workforce may decline by 12%, and average wait times already stretch to 23 days.

AI is no longer optional—it’s essential to scaling diagnostic access. Fortunately, screening is what AI does best: detecting subtle patterns from routine data and enabling earlier, faster triage. The value proposition is shifting—from population health to precision health—allowing for large-scale screening while tailoring care to individual risk profiles. Companies building scalable, accessible diagnostic infrastructure will define the next decade of healthcare delivery.

Clinical Impact

DR is asymptomatic in its early stages and often goes undetected without routine retinal imaging—an exam many patients never receive, especially outside major health systems. In 2018, the FDA addressed this gap by approving IDx-DR, the first autonomous AI diagnostic tool in any field of medicine, specifically for DR screening. It marked a turning point in clinical AI, proving that software could diagnose disease independently of a physician.

Since then, AI tools for DR have advanced rapidly. Clinical research has confirmed that, when properly validated, these systems can match specialist-level accuracy, though concerns remain around generalizability and deployment. The challenge now isn’t performance—it’s distribution. A new wave of platforms is focused on embedding diagnostics directly into everyday care environments.

And the implications go even beyond DR. Google Health’s research has shown that deep learning applied to eye images can predict systemic health indicators, including cardiovascular risk and anemia. Through AI, the eye is becoming a non-invasive diagnostic interface for whole-body health.

Investment Trends

Several companies are translating AI’s diagnostic potential for early DR detection into deployable, real-world infrastructure.

Digital Diagnostics (IDx) was the clinical and regulatory trailblazer. Its FDA-cleared IDx-DR platform was the first autonomous diagnostic system approved in any field of medicine. It analyzes retinal images, provides instant referral decisions, and is broadly integrated into primary care networks.

Eyenuk’s EyeArt system is also FDA-cleared and built around a pay-per-use model for global scalability. It automates DR screening using traditional fundus cameras and is widely adopted in hospital systems and vision clinics.

EyeCheq stands out for pairing diagnostic AI for DR with distribution infrastructure. Rather than relying on fundus cameras or traditional workflows, EyeCheq uses smartphone-compatible imaging to enable DR screening in high-traffic locations—especially through self-service kiosks. Their kiosks now integrate vision testing, DR detection, and even eyewear fulfillment in a compact, autonomous unit. With recent funding and early partnerships in retail and pharmacy environments, EyeCheq is building not just an algorithm, but a physical access layer for AI-driven care.

Eyebot is another kiosk-based player in the race for how consumers access vision eye exams. Its AI-powered, self-serve stations offer eye exams, prescriptions, and eyewear purchases—all without clinical staff. Eyebot’s approach is designed for retail deployment and is already live in commercial settings such as grocery stores. While it’s not focused specifically on DR, its platform is an example of how automated diagnostics are becoming consumer-facing, fast, and scalable.

Retina-AI Health brings a predictive angle to the space. Rather than just identifying DR, its models help forecast progression, giving providers tools to prioritize high-risk patients and manage long-term outcomes.

Toku is pushing the boundaries of what eye data can reveal. Its AI platform uses retinal images to assess cardiovascular and kidney health—treating the eye as a non-invasive window into systemic risk. This shifts the role of retinal imaging from disease detection to preventive health monitoring.
Google Research, though not a commercial entity, is expanding what counts as diagnostic data. Its latest models use external eye photos (i.e., selfies) to infer biomarkers like hemoglobin and liver function, opening new pathways for at-home and contactless diagnostics.

Future Directions

DR is one of the clearest use cases for AI in medicine—high-volume, high-cost, and frequently missed. The future of DR screening is low-friction, embedded, and autonomous.

And it’s also a gateway. AI is beginning to uncover new, non-traditional diagnostic pathways—like the eye—not just for detecting localized disease, but for understanding systemic health risks across the body.

For investors and health systems, the signal is clear: screening will be AI’s foundation in care delivery. DR is the breakthrough example. The bigger opportunity lies in using AI to transform everyday diagnostics into scalable infrastructure—and in rethinking where and how we detect disease.

Ready to reimagine the future of diagnostics? Let’s talk about building the next era of AI-powered healthcare.