U.S. AI-driven Diabetic Retinopathy Screening Market Size, Share & Trends Analysis Report By Component (Software, Hardware, Services), By Screening (Autonomous AI Screening, AI-Assisted Screening), By Deployment Mode, By End Use, And Segment Forecasts, 2026 - 2033
The U.S. AI-driven diabetic retinopathy screening market size was estimated at USD 190.01 million in 2025 and is projected to reach USD 881.74 million by 2033, growing at a CAGR of 21.18% from 2026 to 2033. Rising prevalence of diabetes, favorable reimbursement pathways, and shortage of ophthalmologists and access gaps are significant factors contributing to market growth.
The country faces a growing public health challenge from diabetes, thereby increasing the population at risk of diabetic retinopathy. For instance, according to the data published by the U.S. Centers for Disease Control and Prevention in May 2024, around 38.4 million people were affected by diabetes, accounting for 11.6% of the total U.S. population. Furthermore, the American Academy of Ophthalmology reports that nearly 60 percent of individuals with diabetes do not attend their recommended annual dilated eye examinations, despite established clinical guidelines. This discrepancy between recommended care and actual adherence substantially elevates the risk of undiagnosed disease progression and preventable vision loss.
Diabetic patients are commonly managed in primary care or endocrinology settings, where retinal screening is frequently unavailable. As the prevalence of diabetes increases, the demand for annual eye examinations surpasses the capacity of available specialists. This results in a screening burden that conventional healthcare systems cannot address efficiently. Artificial intelligence-enabled diabetic retinopathy screening systems provide scalable, point-of-care solutions that do not require immediate specialist intervention. AI addresses detection gaps through autonomous and rapid diagnostics. Moreover, primary care integration expands access beyond specialists, enabling early intervention to prevent vision loss and comorbidities. For instance, in July 2023, Mount Sinai launched the Center for Ophthalmic Artificial Intelligence and Human Health, the first in New York, to advance AI in ophthalmology for timely diagnosis of macular degeneration, diabetic retinopathy, glaucoma, hypertensive retinopathy, and retinal tumors. Partnering with the Windreich Department of AI and Human Health, it targets tele-retina, tele-ophthalmology, and eye stroke services using validated AI models.
Furthermore, in 2021, AI-driven eye disease diagnosis advanced significantly with the introduction of a new reimbursement code for AI-based diabetic retinopathy screening in the U.S. Medicare reimbursement accelerated the adoption of AI-based diabetic retinopathy screening in the country through CPT 92229, the first AI-specific code allowing primary care billing without specialist oversight. For instance, LumineticsCore (Digital Diagnostics), EyeArt (Eyenuk), and AEYE-DS (AEYE Health) have each received coverage as autonomous diagnostic systems. By authorizing reimbursement without direct physician interpretation, the Centers for Medicare & Medicaid Services (CMS) has recognized AI as a reimbursable clinical service rather than an experimental adjunct. These policy changes support workflow decentralization and enable screening at the point of care during routine diabetes visits. As a result, providers are more willing to invest in AI-enabled retinal imaging systems, since these services generate predictable revenue and advance quality care objectives.
U.S. AI-driven Diabetic Retinopathy Screening Market Report Segmentation
This report forecasts, revenue growth at country level and provides an analysis of the latest industry trends in each of the sub-segments from 2021 to 2033. For this study, Grand View Research has segmented U.S. AI-driven diabetic retinopathy screening market report based on component, deployment mode, screening, and end use.
This report can be delivered to the clients within 3 Business Days
The country faces a growing public health challenge from diabetes, thereby increasing the population at risk of diabetic retinopathy. For instance, according to the data published by the U.S. Centers for Disease Control and Prevention in May 2024, around 38.4 million people were affected by diabetes, accounting for 11.6% of the total U.S. population. Furthermore, the American Academy of Ophthalmology reports that nearly 60 percent of individuals with diabetes do not attend their recommended annual dilated eye examinations, despite established clinical guidelines. This discrepancy between recommended care and actual adherence substantially elevates the risk of undiagnosed disease progression and preventable vision loss.
Diabetic patients are commonly managed in primary care or endocrinology settings, where retinal screening is frequently unavailable. As the prevalence of diabetes increases, the demand for annual eye examinations surpasses the capacity of available specialists. This results in a screening burden that conventional healthcare systems cannot address efficiently. Artificial intelligence-enabled diabetic retinopathy screening systems provide scalable, point-of-care solutions that do not require immediate specialist intervention. AI addresses detection gaps through autonomous and rapid diagnostics. Moreover, primary care integration expands access beyond specialists, enabling early intervention to prevent vision loss and comorbidities. For instance, in July 2023, Mount Sinai launched the Center for Ophthalmic Artificial Intelligence and Human Health, the first in New York, to advance AI in ophthalmology for timely diagnosis of macular degeneration, diabetic retinopathy, glaucoma, hypertensive retinopathy, and retinal tumors. Partnering with the Windreich Department of AI and Human Health, it targets tele-retina, tele-ophthalmology, and eye stroke services using validated AI models.
Furthermore, in 2021, AI-driven eye disease diagnosis advanced significantly with the introduction of a new reimbursement code for AI-based diabetic retinopathy screening in the U.S. Medicare reimbursement accelerated the adoption of AI-based diabetic retinopathy screening in the country through CPT 92229, the first AI-specific code allowing primary care billing without specialist oversight. For instance, LumineticsCore (Digital Diagnostics), EyeArt (Eyenuk), and AEYE-DS (AEYE Health) have each received coverage as autonomous diagnostic systems. By authorizing reimbursement without direct physician interpretation, the Centers for Medicare & Medicaid Services (CMS) has recognized AI as a reimbursable clinical service rather than an experimental adjunct. These policy changes support workflow decentralization and enable screening at the point of care during routine diabetes visits. As a result, providers are more willing to invest in AI-enabled retinal imaging systems, since these services generate predictable revenue and advance quality care objectives.
U.S. AI-driven Diabetic Retinopathy Screening Market Report Segmentation
This report forecasts, revenue growth at country level and provides an analysis of the latest industry trends in each of the sub-segments from 2021 to 2033. For this study, Grand View Research has segmented U.S. AI-driven diabetic retinopathy screening market report based on component, deployment mode, screening, and end use.
- Component Outlook (Revenue, USD Million, 2021 - 2033)
- Software
- Hardware
- Services
- Deployment Mode Outlook (Revenue, USD Million, 2021 - 2033)
- Cloud-Based
- On-Premise
- Hybrid
- Screening Outlook (Revenue, USD Million, 2021 - 2033)
- Autonomous AI Screening
- AI-Assisted Screening
- Teleophthalmology-Based Screening
- End Use Outlook (Revenue, USD Million, 2021 - 2033)
- Primary Care Settings
- Hospitals
- Ophthalmic Clinics
- Teleophthalmology Providers
- Others
This report can be delivered to the clients within 3 Business Days
CHAPTER 1. METHODOLOGY AND SCOPE
1.1. Market Segmentation & Scope
1.2. Market Definitions
1.2.1. Component Mode Segment
1.2.2. Deployment Mode Segment
1.2.3. Screening Segment
1.2.4. End Use
1.3. Information analysis
1.3.1. Market formulation & data visualization
1.4. Data validation & publishing
1.5. Information Procurement
1.5.1. Primary Research
1.6. Information or Data Analysis
1.7. Market Formulation & Validation
1.8. Market Model
1.9. Total Market: CAGR Calculation
1.10. Objectives
1.10.1. Objective
1.10.2. Objective
CHAPTER 2. EXECUTIVE SUMMARY
2.1. Market Outlook
2.2. Segment Snapshot
2.3. Competitive Insights Landscape
CHAPTER 3. U.S. AI-DRIVEN DIABETIC RETINOPATHY SCREENING MARKET VARIABLES, TRENDS & SCOPE
3.1. Market Lineage Outlook
3.1.1. Parent market outlook
3.1.2. Related/ancillary market outlook.
3.2. Market Dynamics
3.2.1. Market driver analysis
3.2.2. Market restraint analysis
3.2.3. Market opportunity analysis
3.2.4. Market challenges analysis
3.3. AI-driven Diabetic Retinopathy Screening Market Analysis Tools
3.3.1. Industry Analysis - Porter’s
3.3.1.1. Supplier power
3.3.1.2. Buyer power
3.3.1.3. Substitution threat
3.3.1.4. Threat of new entrant
3.3.1.5. Competitive rivalry
3.3.2. PESTEL Analysis
3.3.2.1. Political landscape
3.3.2.2. Technological landscape
3.3.2.3. Economic landscape
3.3.2.4. Environmental Landscape
3.3.2.5. Legal Landscape
3.3.2.6. Social Landscape
3.4. Case Studies
3.5. Technology Overview
CHAPTER 4. U.S. AI-DRIVEN DIABETIC RETINOPATHY SCREENING MARKET: COMPONENT ESTIMATES & TREND ANALYSIS
4.1. Segment Dashboard
4.2. U.S. AI-driven Diabetic Retinopathy Screening Market Component Movement Analysis
4.3. U.S. AI-driven Diabetic Retinopathy Screening Market Size & Trend Analysis, by Component, 2021 to 2033 (USD Million)
4.4. Software
4.4.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
4.5. Hardware
4.5.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
4.6. Services
4.6.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
CHAPTER 5. U.S. AI-DRIVEN DIABETIC RETINOPATHY SCREENING MARKET: SCREENING ESTIMATES & TREND ANALYSIS
5.1. Segment Dashboard
5.2. U.S. AI-driven Diabetic Retinopathy Screening Market Screening Movement Analysis
5.3. U.S. AI-driven Diabetic Retinopathy Screening Market Size & Trend Analysis, by Screening, 2021 to 2033 (USD Million)
5.4. Autonomous AI Screening
5.4.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
5.5. AI-Assisted Screening
5.5.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
5.6. Teleophthalmology-Based Screening
5.6.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
CHAPTER 6. U.S. AI-DRIVEN DIABETIC RETINOPATHY SCREENING MARKET: DEPLOYMENT MODE ESTIMATES & TREND ANALYSIS
6.1. Segment Dashboard
6.2. U.S. AI-driven Diabetic Retinopathy Screening Market Deployment Mode Movement Analysis
6.3. U.S. AI-driven Diabetic Retinopathy Screening Market Size & Trend Analysis, by Deployment Mode, 2021 to 2033 (USD Million)
6.4. Cloud-Based
6.4.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
6.5. On-Premise
6.5.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
6.6. Hybrid
6.6.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
CHAPTER 7. AI-DRIVEN DIABETIC RETINOPATHY SCREENING MARKET: END USE ESTIMATES & TREND ANALYSIS
7.1. Segment Dashboard
7.2. U.S. AI-driven Diabetic Retinopathy Screening Market End Use Movement Analysis
7.3. U.S. AI-driven Diabetic Retinopathy Screening Market Size & Trend Analysis, by End Use, 2021 to 2033 (USD Million)
7.4. Hospitals
7.4.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
7.5. Ophthalmic Clinics
7.5.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
7.6. Primary Care Settings
7.6.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
7.7. Teleophthalmology Providers
7.7.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
7.8. Others
7.8.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
CHAPTER 8. COMPETITIVE LANDSCAPE
8.1. Company/Competition Categorization
8.2. Strategy Mapping
8.3. Company Market Position Analysis, 2025
8.4. Company Profiles/Listing
8.4.1. Eyenuk, Inc.
8.4.1.1. Company overview
8.4.1.2. Financial performance
8.4.1.3. Product benchmarking
8.4.1.4. Strategic initiatives
8.4.2. Digital Diagnostics Inc.
8.4.2.1. Company overview
8.4.2.2. Financial performance
8.4.2.3. Product benchmarking
8.4.2.4. Strategic initiatives
8.4.3. AEYE Health.
8.4.3.1. Company overview
8.4.3.2. Financial performance
8.4.3.3. Product benchmarking
8.4.3.4. Strategic initiatives
8.4.4. Optomed
8.4.4.1. Company overview
8.4.4.2. Financial performance
8.4.4.3. Product benchmarking
8.4.4.4. Strategic initiatives
8.4.5. IRIS (Intelligent Retinal Imaging Systems)
8.4.5.1. Company overview
8.4.5.2. Financial performance
8.4.5.3. Product benchmarking
8.4.5.4. Strategic initiatives
8.4.6. RETINA-AI Health, Inc.
8.4.6.1. Company overview
8.4.6.2. Financial performance
8.4.6.3. Product benchmarking
8.4.6.4. Strategic initiatives
8.4.7. iCare
8.4.7.1. Company overview
8.4.7.2. Financial performance
8.4.7.3. Product benchmarking
8.4.7.4. Strategic initiatives
8.4.8. RetinaRisk (by Risk Medical Solutions)
8.4.8.1. Company overview
8.4.8.2. Financial performance
8.4.8.3. Product benchmarking
8.4.8.4. Strategic initiatives
8.4.9. BeamMed Inc.
8.4.9.1. Company overview
8.4.9.2. Financial performance
8.4.9.3. Product benchmarking
8.4.9.4. Strategic initiatives
1.1. Market Segmentation & Scope
1.2. Market Definitions
1.2.1. Component Mode Segment
1.2.2. Deployment Mode Segment
1.2.3. Screening Segment
1.2.4. End Use
1.3. Information analysis
1.3.1. Market formulation & data visualization
1.4. Data validation & publishing
1.5. Information Procurement
1.5.1. Primary Research
1.6. Information or Data Analysis
1.7. Market Formulation & Validation
1.8. Market Model
1.9. Total Market: CAGR Calculation
1.10. Objectives
1.10.1. Objective
1.10.2. Objective
CHAPTER 2. EXECUTIVE SUMMARY
2.1. Market Outlook
2.2. Segment Snapshot
2.3. Competitive Insights Landscape
CHAPTER 3. U.S. AI-DRIVEN DIABETIC RETINOPATHY SCREENING MARKET VARIABLES, TRENDS & SCOPE
3.1. Market Lineage Outlook
3.1.1. Parent market outlook
3.1.2. Related/ancillary market outlook.
3.2. Market Dynamics
3.2.1. Market driver analysis
3.2.2. Market restraint analysis
3.2.3. Market opportunity analysis
3.2.4. Market challenges analysis
3.3. AI-driven Diabetic Retinopathy Screening Market Analysis Tools
3.3.1. Industry Analysis - Porter’s
3.3.1.1. Supplier power
3.3.1.2. Buyer power
3.3.1.3. Substitution threat
3.3.1.4. Threat of new entrant
3.3.1.5. Competitive rivalry
3.3.2. PESTEL Analysis
3.3.2.1. Political landscape
3.3.2.2. Technological landscape
3.3.2.3. Economic landscape
3.3.2.4. Environmental Landscape
3.3.2.5. Legal Landscape
3.3.2.6. Social Landscape
3.4. Case Studies
3.5. Technology Overview
CHAPTER 4. U.S. AI-DRIVEN DIABETIC RETINOPATHY SCREENING MARKET: COMPONENT ESTIMATES & TREND ANALYSIS
4.1. Segment Dashboard
4.2. U.S. AI-driven Diabetic Retinopathy Screening Market Component Movement Analysis
4.3. U.S. AI-driven Diabetic Retinopathy Screening Market Size & Trend Analysis, by Component, 2021 to 2033 (USD Million)
4.4. Software
4.4.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
4.5. Hardware
4.5.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
4.6. Services
4.6.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
CHAPTER 5. U.S. AI-DRIVEN DIABETIC RETINOPATHY SCREENING MARKET: SCREENING ESTIMATES & TREND ANALYSIS
5.1. Segment Dashboard
5.2. U.S. AI-driven Diabetic Retinopathy Screening Market Screening Movement Analysis
5.3. U.S. AI-driven Diabetic Retinopathy Screening Market Size & Trend Analysis, by Screening, 2021 to 2033 (USD Million)
5.4. Autonomous AI Screening
5.4.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
5.5. AI-Assisted Screening
5.5.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
5.6. Teleophthalmology-Based Screening
5.6.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
CHAPTER 6. U.S. AI-DRIVEN DIABETIC RETINOPATHY SCREENING MARKET: DEPLOYMENT MODE ESTIMATES & TREND ANALYSIS
6.1. Segment Dashboard
6.2. U.S. AI-driven Diabetic Retinopathy Screening Market Deployment Mode Movement Analysis
6.3. U.S. AI-driven Diabetic Retinopathy Screening Market Size & Trend Analysis, by Deployment Mode, 2021 to 2033 (USD Million)
6.4. Cloud-Based
6.4.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
6.5. On-Premise
6.5.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
6.6. Hybrid
6.6.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
CHAPTER 7. AI-DRIVEN DIABETIC RETINOPATHY SCREENING MARKET: END USE ESTIMATES & TREND ANALYSIS
7.1. Segment Dashboard
7.2. U.S. AI-driven Diabetic Retinopathy Screening Market End Use Movement Analysis
7.3. U.S. AI-driven Diabetic Retinopathy Screening Market Size & Trend Analysis, by End Use, 2021 to 2033 (USD Million)
7.4. Hospitals
7.4.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
7.5. Ophthalmic Clinics
7.5.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
7.6. Primary Care Settings
7.6.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
7.7. Teleophthalmology Providers
7.7.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
7.8. Others
7.8.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
CHAPTER 8. COMPETITIVE LANDSCAPE
8.1. Company/Competition Categorization
8.2. Strategy Mapping
8.3. Company Market Position Analysis, 2025
8.4. Company Profiles/Listing
8.4.1. Eyenuk, Inc.
8.4.1.1. Company overview
8.4.1.2. Financial performance
8.4.1.3. Product benchmarking
8.4.1.4. Strategic initiatives
8.4.2. Digital Diagnostics Inc.
8.4.2.1. Company overview
8.4.2.2. Financial performance
8.4.2.3. Product benchmarking
8.4.2.4. Strategic initiatives
8.4.3. AEYE Health.
8.4.3.1. Company overview
8.4.3.2. Financial performance
8.4.3.3. Product benchmarking
8.4.3.4. Strategic initiatives
8.4.4. Optomed
8.4.4.1. Company overview
8.4.4.2. Financial performance
8.4.4.3. Product benchmarking
8.4.4.4. Strategic initiatives
8.4.5. IRIS (Intelligent Retinal Imaging Systems)
8.4.5.1. Company overview
8.4.5.2. Financial performance
8.4.5.3. Product benchmarking
8.4.5.4. Strategic initiatives
8.4.6. RETINA-AI Health, Inc.
8.4.6.1. Company overview
8.4.6.2. Financial performance
8.4.6.3. Product benchmarking
8.4.6.4. Strategic initiatives
8.4.7. iCare
8.4.7.1. Company overview
8.4.7.2. Financial performance
8.4.7.3. Product benchmarking
8.4.7.4. Strategic initiatives
8.4.8. RetinaRisk (by Risk Medical Solutions)
8.4.8.1. Company overview
8.4.8.2. Financial performance
8.4.8.3. Product benchmarking
8.4.8.4. Strategic initiatives
8.4.9. BeamMed Inc.
8.4.9.1. Company overview
8.4.9.2. Financial performance
8.4.9.3. Product benchmarking
8.4.9.4. Strategic initiatives
LIST OF TABLES
Table 1 List of abbreviations
Table 2 U.S. AI-driven diabetic retinopathy screening market, by region, 2021 - 2033 (USD Million)
Table 3 U.S. AI-driven diabetic retinopathy screening market, by component, 2021 - 2033 (USD Million)
Table 4 U.S. AI-driven diabetic retinopathy screening market, by deployment mode, 2021 - 2033 (USD Million)
Table 5 U.S. AI-driven diabetic retinopathy screening market, by screening, 2021 - 2033 (USD Million)
Table 6 U.S. AI-driven diabetic retinopathy screening market, by end use, 2021 - 2033 (USD Million)
Table 1 List of abbreviations
Table 2 U.S. AI-driven diabetic retinopathy screening market, by region, 2021 - 2033 (USD Million)
Table 3 U.S. AI-driven diabetic retinopathy screening market, by component, 2021 - 2033 (USD Million)
Table 4 U.S. AI-driven diabetic retinopathy screening market, by deployment mode, 2021 - 2033 (USD Million)
Table 5 U.S. AI-driven diabetic retinopathy screening market, by screening, 2021 - 2033 (USD Million)
Table 6 U.S. AI-driven diabetic retinopathy screening market, by end use, 2021 - 2033 (USD Million)
LIST OF FIGURES
Fig. 1 Market research process
Fig. 2 Market research process
Fig. 3 Data triangulation techniques
Fig. 4 Market formulation & validation
Fig. 5 U.S. AI-driven diabetic retinopathy screening market: Market outlook
Fig. 6 U.S. AI-driven diabetic retinopathy screening market: Segment outlook
Fig. 7 U.S. AI-driven diabetic retinopathy screening market: Competitive landscape outlook
Fig. 8 Parent market outlook
Fig. 9 U.S. AI-driven diabetic retinopathy screening market driver impact
Fig. 10 U.S. AI-driven diabetic retinopathy screening market restraint impact
Fig. 11 U.S. AI-driven diabetic retinopathy screening market: Component outlook and key takeaways
Fig. 12 U.S. AI-driven diabetic retinopathy screening market: Component movement analysis
Fig. 13 Software market estimates and forecasts, 2021 - 2033 (USD Million)
Fig. 14 Hardware market estimates and forecasts, 2021 - 2033 (USD Million)
Fig. 15 Services market estimates and forecasts, 2021 - 2033 (USD Million)
Fig. 16 U.S. AI-driven diabetic retinopathy screening market: Screening outlook and key takeaways
Fig. 17 U.S. AI-driven diabetic retinopathy screening market: Screening movement analysis
Fig. 18 Autonomous AI screening market estimates and forecasts, 2021 - 2033 (USD Million)
Fig. 19 AI-assisted screening market estimates and forecasts, 2021 - 2033 (USD Million)
Fig. 20 Teleophthalmology-based screening market estimates and forecasts, 2021 - 2033 (USD Million)
Fig. 21 AI-driven diabetic retinopathy screening market: Deployment mode outlook and key takeaways
Fig. 22 AI-driven diabetic retinopathy screening market: Deployment mode movement analysis
Fig. 23 Cloud-based market estimates and forecasts, 2021 - 2033 (USD Million)
Fig. 24 On-premise market estimates and forecasts, 2021 - 2033 (USD Million)
Fig. 25 Hybrid market estimates and forecasts, 2021 - 2033 (USD Million)
Fig. 26 AI-driven diabetic retinopathy screening market: End use outlook and key takeaways
Fig. 27 AI-driven diabetic retinopathy screening market: End use movement analysis
Fig. 28 Hospitals market estimates and forecasts, 2021 - 2033 (USD Million)
Fig. 29 Ophthalmic clinics market estimates and forecasts, 2021 - 2033 (USD Million)
Fig. 30 Primary care settings market estimates and forecasts, 2021 - 2033 (USD Million)
Fig. 31 Teleophthalmology providers market estimates and forecasts, 2021 - 2033 (USD Million)
Fig. 32 Others market estimates and forecasts, 2021 - 2033 (USD Million)
Fig. 1 Market research process
Fig. 2 Market research process
Fig. 3 Data triangulation techniques
Fig. 4 Market formulation & validation
Fig. 5 U.S. AI-driven diabetic retinopathy screening market: Market outlook
Fig. 6 U.S. AI-driven diabetic retinopathy screening market: Segment outlook
Fig. 7 U.S. AI-driven diabetic retinopathy screening market: Competitive landscape outlook
Fig. 8 Parent market outlook
Fig. 9 U.S. AI-driven diabetic retinopathy screening market driver impact
Fig. 10 U.S. AI-driven diabetic retinopathy screening market restraint impact
Fig. 11 U.S. AI-driven diabetic retinopathy screening market: Component outlook and key takeaways
Fig. 12 U.S. AI-driven diabetic retinopathy screening market: Component movement analysis
Fig. 13 Software market estimates and forecasts, 2021 - 2033 (USD Million)
Fig. 14 Hardware market estimates and forecasts, 2021 - 2033 (USD Million)
Fig. 15 Services market estimates and forecasts, 2021 - 2033 (USD Million)
Fig. 16 U.S. AI-driven diabetic retinopathy screening market: Screening outlook and key takeaways
Fig. 17 U.S. AI-driven diabetic retinopathy screening market: Screening movement analysis
Fig. 18 Autonomous AI screening market estimates and forecasts, 2021 - 2033 (USD Million)
Fig. 19 AI-assisted screening market estimates and forecasts, 2021 - 2033 (USD Million)
Fig. 20 Teleophthalmology-based screening market estimates and forecasts, 2021 - 2033 (USD Million)
Fig. 21 AI-driven diabetic retinopathy screening market: Deployment mode outlook and key takeaways
Fig. 22 AI-driven diabetic retinopathy screening market: Deployment mode movement analysis
Fig. 23 Cloud-based market estimates and forecasts, 2021 - 2033 (USD Million)
Fig. 24 On-premise market estimates and forecasts, 2021 - 2033 (USD Million)
Fig. 25 Hybrid market estimates and forecasts, 2021 - 2033 (USD Million)
Fig. 26 AI-driven diabetic retinopathy screening market: End use outlook and key takeaways
Fig. 27 AI-driven diabetic retinopathy screening market: End use movement analysis
Fig. 28 Hospitals market estimates and forecasts, 2021 - 2033 (USD Million)
Fig. 29 Ophthalmic clinics market estimates and forecasts, 2021 - 2033 (USD Million)
Fig. 30 Primary care settings market estimates and forecasts, 2021 - 2033 (USD Million)
Fig. 31 Teleophthalmology providers market estimates and forecasts, 2021 - 2033 (USD Million)
Fig. 32 Others market estimates and forecasts, 2021 - 2033 (USD Million)
