Revenue, 2025

USD 191.05 Million

Forecast, 2035

USD 1308.85 Million

CAGR, 2026-2035

21.11%

Report Coverage

United States

U.S. AI-Driven Diabetic Retinopathy Screening Market Trends for 2026

The U.S. AI-driven diabetic retinopathy screening market size was estimated at USD 191.05 million in 2025 and is predicted to increase from USD 231.59 million in 2026 to approximately USD 1308.85 million by 2035, expanding at a CAGR of 21.22% from 2026 to 2035.

The market is expanding as more healthcare providers adopt automated retinal analysis tools to detect vision-threatening complications earlier, improve efficiency, and reach underserved patients amid rising diabetes prevalence and technological advances.

Key Takeaways

• The United States AI-driven diabetic retinopathy screening market will likely exceed USD 231.59 million by 2026.
• Valuation is projected to hit USD 1308.85 million by 2035.
• Estimated to grow at a CAGR of 21.22% starting from 2026 to 2035.
• By component, the software segment registered dominance in the market in 2025.  
• By component, the services segment is expected to grow at the fastest CAGR in the market during the forecast period.
• By deployment mode, the cloud-based segment dominated the market in 2025.
• By deployment mode, the hybrid segment is expected to grow at the fastest CAGR in the market during the forecast period.
• By screening, the autonomous AI screening segment holds a dominant position in the U.S. AI-driven diabetic retinopathy screening market in 2025.
• By screening, the teleophthalmology-based screening segment is expected to grow at the fastest CAGR in the market during the forecast period.
• By end use, the primary care settings segment led the market in 2025.
• By end use, the teleophthalmology providers segment is expected to grow at the fastest CAGR in the market during the forecast period.

Why is the U.S. AI-Driven Diabetic Retinopathy screening market growing?

U.S. AI-driven diabetic retinopathy screening is the use of artificial intelligence technology to automatically detect and analyze retinal changes in diabetic patients, enabling early diagnosis and timely treatment of vision-threatening eye complications. The U.S. market for AI-driven diabetic retinopathy screening is expanding as high diabetes rates increase demand for efficient eye care. AI tools help detect retinal damage early, reduce reliance on specialists, speed diagnosis, and lower costs. Supportive FDA approvals and reimbursement trends are encouraging adoption in clinics, hospitals, and telehealth improving access and outcomes for patients at risk of vision loss.

Trends and Future Outlook of the U.S. AI-Driven Diabetic Retinopathy Screening Market

• Rising Adoption in Primary Care Settings: AI-based retinal screening is increasingly being used in primary care clinics rather than only ophthalmology centers. This trend improves early detection of diabetic eye disease and allows faster screening for large patient populations without requiring specialist availability.
• Integration with Telehealth and Digital Platforms: AI screening tools are being integrated with telehealth systems and digital health platforms. This enables remote retinal analysis, improves access for patients in underserved regions, and allows healthcare providers to manage and monitor diabetic eye conditions more efficiently.
• Advancements in AI Algorithms and Imaging: Continuous improvements in AI algorithms and retinal imaging technologies are enhancing diagnostic accuracy and speed. These advancements are expected to strengthen the reliability of automated screening systems and support wider adoption across hospitals, clinics, and diagnostic centers.

Quick Facts Table

Segmental Insights

By Component Insights

Why Did the Software Segment Dominate in the Market in 2025?

The software segment dominated the U.S. AI-driven diabetic retinopathy screening market in 2025 due to the increasing adoption of AI-based diagnostic platforms that enable faster and more accurate detection of retinal abnormalities. Healthcare providers prefer software solutions because they integrate easily with existing imaging systems and electronic health records. Additionally, continuous advancements in AI algorithms and cloud-based screening tools improved efficiency, scalability, and accessibility in ophthalmology practices and clinics.

Services

The services segment is expected to grow at the fastest CAGR during the forecast period due to the increasing demand for implementation, maintenance, training, and technical support for AI-driven screening systems. Healthcare providers require specialized services to effectively integrate AI software with existing diagnostic infrastructure. Additionally, the rising adoption of AI-based healthcare solutions and the need for continuous systems upgrades and data management are driving the growth of services-based offerings in the market.

By Deployment Mode Insights

What Made the Cloud-Based Segment Dominant in the Market in 2025?

The cloud-based segment dominated the U.S. AI-driven diabetic retinopathy screening market in 2025 due to its ability to support scalable data storage, real-time image analysis, and remote access to diagnostic tools. Healthcare providers increasingly prefer cloud platforms, as they enable seamless integration with teleophthalmology systems and electronic health records. Additionally, cloud deployment reduces infrastructure costs and supports faster updates of AI algorithms, improving screening efficiency and accessibility across healthcare facilities.

Hybrid

The hybrid segment is expected to grow at the fastest CAGR during the forecast period due to its ability to combine the benefits of both cloud and on-premises deployment. Hybrid solutions offer greater flexibility, enhanced data security, and improved control over sensitive patient information. Additionally, healthcare organizations are increasingly adopting hybrid models to balance scalability, compliance requirements, and efficiency in AI-driven screening operations across multiple healthcare facilities.

By Screening Insights

How did the Autonomous AI Screening Segment Dominate the Market in 2025?

The autonomous AI screening segment dominated the U.S. AI-driven diabetic retinopathy screening market in 2025 due to its ability to automatically detect diabetic retinopathy without requiring immediate specialist involvement. These systems provide rapid, accurate analysis of retinal images, improving early diagnosis and reducing the burden on ophthalmologists. Increasing EDA approvals, growing adoption in primary care settings, and the rising need for efficient large-scale diabetes screening programs further support the dominance of autonomous AI screening solutions.

Teleophthalmology-based Screening

The teleophthalmology-based screening segment is expected to grow at the fastest CAGR during the forecast period due to the increasing demand for remote healthcare services and improved access to eye care in underserved and rural areas. Teleophthalmology enables retinal images to be captured at local clinics and analyzed remotely by specialized or AI systems. Additionally, the rising adoption of digital health technologies and telemedicine platforms is accelerating the expansion of remote diabetic retinopathy screening programs.

By End-Use Insights

How Did the Primary Care Settings Segment Dominate the Market in 2025?

The primary care settings segment dominated the U.S. AI-driven diabetic retinopathy screening market in 2025 due to the growing integration of AI screening tools in primary healthcare facilities. These settings allow early detection of diabetic retinopathy during routine diabetes check-ups, improving patient access to timely diagnosis. Additionally, AI-enabled systems reduce the need for specialist involvement, enabling faster screening and increasing adoption among primary care providers and clinics.

Teleophthalmology providers

The teleophthalmology providers segment is expected to grow at the fastest CAGR during the forecast period due to the rising demand for remote eye care services and improved access to screening in rural and underserved regions. Teleophthalmology allows retinal images to be captured locally and evaluated remotely by specialized or AI systems. Additionally, increasing adoption of telemedicine platforms and digital health technologies is accelerating the expansion of remote diabetic retinopathy screening services.

Supply Chain Analysis

R&D

• Research and development for AI-driven diabetic retinopathy screening in the U.S. has shifted from retrospective research to real-world clinical use. Current efforts focus on improving early detection, integrating screening tools into primary care settings, and increasing patient follow-up rates to enhance treatment outcomes.
• Key players: Digital Diagnostics, Eyenuk, Google Health, and IBM Watson Health

Clinical Trials

• Clinical trials studies for AI-based diabetic retinopathy screening in the U.S. have evolved from validating diagnostic accuracy to proving effectiveness in real clinical environments. Current trials emphasize improving screening rates in underserved populations, primary care facilities, and pediatric settings to enhance early detection.
• Key players: Digital Diagnostics, Eyenuk, Google Health, and Medtronic.

Patient Support and Services 

• Patient support and service programs for AI-based diabetic retinopathy screening in the U.S. are designed to expand access to eye care, deliver faster screening results, and improve patient adherence to follow-up care. These initiatives also help reduce healthcare disparities by extending screening services to underserved communities.
• Key players: Topcon Healthcare, Canon Medical Systems, Optomed, and IDx Technologies.

Top Vendors in the U.S. AI-Driven Diabetic Retinopathy Screening Market & Their Offerings

SWOT Analysis

Strengths

• High diagnostic accuracy supports early detection of diabetic retinopathy and improves treatment outcomes.
• Advanced healthcare infrastructure in the U.S. supports the adoption of AI-based screening technologies.
• Increasing regulatory approvals for autonomous AI diagnostic tools encourage wider clinical use.
• Integration of AI screening in primary care improves access to early eye disease detection.

Weaknesses

• The high initial cost of AI software and retinal imaging equipment can limit adoption in smaller clinics.
• Limited technical expertise and training in some healthcare settings may slow implementation.
• Concerns regarding patient data privacy and cybersecurity may affect trust in AI systems.
• AI performance depends on the quality of retinal images captured during screening.

Opportunities

• Rising prevalence of diabetes in the U.S. increases the demand for regular retinopathy screening.
• Expansion of teleophthalmology programs can improve access in rural and underserved areas.
• Ongoing advancements in AI and machine learning technologies can enhance screening accuracy and efficiency.
• Integration with electronic health records can streamline clinical workflows and patient monitoring.

Threats

• Strict regulatory approval processes may delay the launch of new AI-based screening solutions.
• Growing competition among technology providers may create pricing pressure in the market.
• Concerns about potential diagnostic errors and legal liability may impact adoption.
• Some healthcare professionals may be hesitant to rely on automated AI systems for diagnosis.

What are the Recent Developments in the U.S. AI-driven diabetic retinopathy screening market?

• In July 2025, AEYE Health formed a strategic partnership with Ford Medical to expand access to autonomous AI-based diabetic eye screening solutions. Through this collaboration, the companies aim to introduce automated diabetic retinopathy screening services across pharmacies and non-acute healthcare centers in the U.S., helping improve early disease detection and making retinal screening more accessible and efficient for patients.
• In July 2025, Topcon Healthcare acquired Intelligent Retinal Imaging Systems (IRIS) to strengthen its capabilities in AI-based retinal diagnostics. The acquisition supports Topcon’s “Healthcare from the Eye” initiative by enhancing clinical decision-making for diabetic retinopathy. The company plans to integrate IRIS technologies into its existing platforms to improve the accuracy and efficiency of retinal disease detection.

Segments Covered in the Report

By Component Outlook

• Software
• Hardware
• Services

By Deployment Mode

• Cloud-Based
• On-Premise
• Hybrid

By Screening

• Autonomous AI Screening
• AI-Assisted Screening
• Teleophthalmology-Based Screening

By End Use

• Primary Care Settings
• Hospitals
• Ophthalmic Clinics
• Teleophthalmology Providers
• Others