美國人工智慧糖尿病視網膜病變篩檢市場：市場規模、佔有率和趨勢分析（按組件、篩檢方法、部署方法和最終用途分類），細分市場預測（2026-2033 年）

美國人工智慧驅動的糖尿病視網膜病變篩檢市場概述

據估計，到 2025 年，美國人工智慧驅動的糖尿病視網膜病變篩檢市值為 1.9001 億美元，預計到 2033 年將達到 8.8174 億美元。

預計從 2026 年到 2033 年，市場將以 21.18% 的複合年成長率成長。糖尿病盛行率上升、有利的保險報銷方案、眼科醫生短缺以及醫療保健服務取得方面的不平等是市場成長的關鍵促進因素。

在美國，糖尿病已成為日益嚴峻的公共衛生挑戰，導致越來越多的人面臨糖尿病視網膜病變的風險。例如，根據美國疾病管制與預防中心（CDC）2024年5月發布的數據，約有3,840萬人患有糖尿病，占美國總人口的11.6%。此外，美國眼科學會（AAO）的報告顯示，儘管有明確的臨床指南，但仍有近60%的糖尿病患者未接受建議的年度散瞳眼底檢查。這種建議治療與實際依從性之間的差距顯著增加了疾病未確診進展並導致可預防性視力喪失的風險。

雖然糖尿病患者通常在基層醫療或內分泌科接受治療，但這些科室往往無法提供視網膜篩檢。隨著糖尿病盛行率的上升，年度眼科檢查的需求已超過現有專家的服務能力。這導致篩檢負擔沉重，而傳統醫療系統難以有效應對。人工智慧 (AI) 驅動的糖尿病視網膜病變篩檢系統提供了一種擴充性的臨床級解決方案，無需專科醫生即時介入。 AI 透過自主快速診斷彌補了檢測方面的不足。此外，將其整合到基層醫療中，使非專科醫生也能使用該系統，從而實現早期療育，預防視力喪失和併發症。例如，2023 年 7 月，西奈山醫院成立了紐約首個眼科人工智慧與人類健康中心，推動了 AI 在眼科領域的應用，以實現對黃斑部病變、糖尿病視網膜病變、青光眼、高血壓性視網膜病變和視網膜腫瘤的及時診斷。該中心與溫德賴希人工智慧與人類健康學院合作，利用檢驗的人工智慧模型，推廣遠端視網膜診斷、遠端眼科諮詢和眼科中風治療。

此外，2021年，美國聯邦醫療保險（Medicare）引入了新的人工智慧糖尿病視網膜病變篩檢報銷代碼，顯著推進了人工智慧輔助眼科疾病診斷的發展。代碼為CPT 92229，是首個無需專科醫生監督即可在基層醫療報銷的人工智慧專用代碼，加速了人工智慧糖尿病視網膜病變篩檢在美國的普及。例如，LumineticsCore（Digital Diagnostics）、EyeArt（Eyenuk）和AEYE-DS（AEYE Health）等系統皆以獨立的診斷系統納入報銷範圍。透過核准無需醫生直接解讀即可報銷，美國聯邦醫療保險和醫療補助服務中心（CMS）認可人工智慧並非實驗性輔助手段，而是可報銷的臨床服務。這些政策變更有助於工作流程分散化，並支持在糖尿病門診常規就診期間進行現場篩檢。因此，這些服務能夠產生可預測的收入，醫療保健提供者也更積極地投資於人工智慧驅動的視網膜成像系統，以推動高品質醫療保健目標的實現。

目錄

第1章：分析方法和範圍

第2章執行摘要

第3章：美國人工智慧糖尿病視網膜病變篩檢市場：影響因素、趨勢和範圍

• 市場歷史及展望
  • 母市場展望
  • 相關/附隨市場展望
• 市場動態
• 人工智慧驅動的糖尿病視網膜病變篩檢市場：分析工具
  • 產業分析：波特五力分析
  • PESTEL 分析
• 案例研究
• 技術概述

第4章：美國人工智慧糖尿病視網膜病變篩檢市場：按組件分類的估算和趨勢分析

• 美國人工智慧糖尿病視網膜病變篩檢市場：按組件分類的波動分析
• 美國人工智慧糖尿病視網膜病變篩檢市場：市場規模和趨勢分析：按組成部分分類（2021-2033 年）
• 軟體
• 硬體
• 服務

第5章：美國人工智慧糖尿病視網膜病變篩檢市場：按篩檢方法分類的估算和趨勢分析

• 美國人工智慧糖尿病視網膜病變篩檢市場：按篩檢方法分類的波動分析
• 美國人工智慧糖尿病視網膜病變篩檢市場：市場規模和趨勢分析：按篩檢方法分類（2021-2033 年）
• 自主人工智慧篩檢
• 人工智慧輔助篩檢
• 基於遠距眼科的篩檢

第6章：美國人工智慧糖尿病視網膜病變篩檢市場：按部署方式分類的估算與趨勢分析

• 美國人工智慧糖尿病視網膜病變篩檢市場：按部署方式分類的波動分析
• 美國人工智慧糖尿病視網膜病變篩檢市場：市場規模與趨勢分析：按部署方式分類（2021-2033 年）
• 雲
• 現場
• 混合

第7章：美國人工智慧糖尿病視網膜病變篩檢市場：按最終用途分類的估算和趨勢分析

• 美國人工智慧糖尿病視網膜病變篩檢市場：按最終用途分類的波動分析
• 美國人工智慧糖尿病視網膜病變篩檢市場：市場規模和趨勢分析：按最終用途分類（2021-2033 年）
• 醫院
• 眼科診所
• 基層醫療機構
• 遠距眼科醫療保健提供者
• 其他

第8章 競爭情勢

• 公司/競爭對手分類
• 策略規劃
• 企業市場分析（2025 年）
• 公司簡介/列表
  • Eyenuk, Inc.
  • Digital Diagnostics Inc.
  • AEYE Health.
  • Optomed
  • IRIS（Intelligent Retinal Imaging Systems）
  • RETINA-AI Health, Inc.
  • iCare
  • RetinaRisk（by Risk Medical Solutions）
  • BeamMed Inc.

U.S. AI-driven Diabetic Retinopathy Screening Market Summary

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.

• 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

Table of Contents

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
  • 1.10.2. Objective 2

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)

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)