세계의 안과용 AI 시장 규모, 점유율, 동향 분석 보고서 : 용도별, 전개 모드별, 기술별, 최종 용도별, 지역별, 부문별 예측(2025-2030년)
AI In Ophthalmology Market Size, Share & Trends Analysis Report By Application (Disease Detection & Monitoring, Surgical Planning & Outcome Prediction), By Deployment Mode, By Technolog, By End-use, By Region, And Segment Forecasts, 2025 - 2030
세계의 안과용 AI 시장 개요
세계의 안과용 AI 시장 규모는 2024년 2억 923만 달러로 추정되었고 2025-2030년까지 36.79%의 연평균 복합 성장률(CAGR)로 성장하여 2030년 13억 6,000만 달러에 이를 것으로 예측되고 있습니다.
게다가, 맞춤 치료 계획에 대한 선호도 증가와 정부 이니셔티브 증가가 시장 성장을 더욱 뒷받침하고 있습니다. 당뇨병성 망막증, 나이 관련 황반변성(AMD), 녹내장 등 눈과 관련된 질병의 유병률 증가는 안과에서 AI의 채용을 촉진하는 중요한 요인입니다. 예를 들어, CDC에 따르면, 2022년에 녹내장을 앓고 있는 미국인의 추정 수는 422만 명이었습니다.
게다가 광간섭 단층계(OCT)와 같은 첨단 영상 기술을 AI와 통합함으로써 안과 진단에 혁명을 가져왔습니다. 예를 들어 홍콩 중문대학(CUHK)의 연구자는 첨단 AI 안과 이미지 기반 모델인 VisionFM을 개발했습니다.
게다가 안과 의료 서비스의 원격 제공인 원격 안과 의료는 특히 충분한 서비스를 받지 않는 지역에서 지지를 모으고 있습니다. 이를 통해 이 확장은 매우 중요합니다. 2024년 6월, C3 Med-Tech는 AI 대응 휴대용 안과 검진 장비를 출시하기 위해 23만 달러를 조달했습니다.
게다가 전자기술(EHR)로부터의 데이터를 분석 및 해석하는 AI의 능력은 안과에서의 맞춤 치료계획을 촉진합니다.
목차
제1장 조사 방법과 범위
제2장 주요 요약
제3장 안과용 AI 시장의 변수, 동향, 범위
- 시장 계통의 전망
- 시장 역학
- 시장 성장 촉진요인 분석
- 시장 성장 억제요인 분석
- 시장 기회 분석
- 시장 과제 분석
- 이용 사례
- 안과 시장 분석 도구의 AI
- 산업 분석 - Porter's Five Forces 분석
- PESTEL 분석
제4장 안과용 AI 시장 : 용도별, 추정 및 동향 분석
- 부문 대시보드
- 안과용 AI 세계 시장 용도 변동 분석
- 안과용 AI 세계 시장 규모와 동향 분석(용도별, 2018-2030년)
- 질병의 검출과 모니터링
- 수술 계획과 결과 예측
- 안과 영상 진단 워크플로우 자동화를 위한 AI
- 기타
제5장 안과용 AI 시장 : 전개 모드, 추정 및 동향 분석
- 부문 대시보드
- 안과용 AI 시장 전개 모드 변동 분석
- 안과용 AI 세계 시장 규모와 동향 분석(전개 모드별, 2018-2030년)
- On-Premise
- 클라우드 기반
제6장 안과용 AI 시장 : 기술별, 추정 및 동향 분석
- 부문 대시보드
- 안과용 AI 시장 기술 변동 분석
- 안과용 AI 세계 시장 규모와 동향 분석(기술별, 2018-2030년)
- 머신러닝
- 자연언어처리
- 임상 문서 작성 지원
- OCR(광학 문자 인식)
- 안과 노트 자동 코딩
- 진단 추론을 위한 텍스트 분석
- 음성 기반 진단 기록(음성 텍스트 변환)
- 컨텍스트 인식 컴퓨팅
- 컴퓨터 비전
제7장 안과용 AI 시장 : 최종 용도별, 추정 및 동향 분석
- 부문 대시보드
- 안과용 AI 시장 최종 용도 변동 분석
- 안과용 AI 세계 시장 규모와 동향 분석(최종 용도별, 2018-2030년)
- 병원
- 전문 안과 클리닉
- 학술연구기관
- 지불자 및 보험사
- 기타
제8장 안과용 AI 시장 : 지역별, 추정 및 동향 분석
- 지역별 시장 점유율 분석, 2024년 및 2030년
- 지역별 시장 대시보드
- 시장 규모와 예측 동향 분석, 2018-2030년
- 북미
- 유럽
- 영국
- 독일
- 프랑스
- 이탈리아
- 스페인
- 노르웨이
- 스웨덴
- 덴마크
- 아시아태평양
- 라틴아메리카
- 중동 및 아프리카
- 남아프리카
- 사우디아라비아
- 아랍에미리트(UAE)
- 쿠웨이트
제9장 경쟁 구도
- 기업/경쟁의 분류
- 전략 매핑
- 기업시장 포지셔닝 분석, 2024년
- 기업 프로파일/상장 기업
- OphtAI
- Eyenuk, Inc.
- Google LLC
- IBM Corporation
- Optos plc
- Zeiss
- Topcon Healthcare
- Ikerian AG(RetinAi)
- Nidek Co., Ltd.
- Altris AI
- Remidio Innovative Solutions Pvt Ltd.
- Oculus Maxima LIMITED
- Siemens Healthineers
- Haag-Streit Group
>JHS
AI In Ophthalmology Market Summary
The global AI in ophthalmology market size was estimated at USD 209.23 million in 2024 and is projected to reach USD 1.36 billion by 2030, growing at a CAGR of 36.79% from 2025 to 2030. The rising prevalence of eye diseases, advancements in imaging technology, and expansion of teleophthalmology services are factors contributing to market growth.
In addition, growing preference for personalized treatment plans and increasing government initiatives fuel market growth further. The increasing prevalence of eye-related conditions, such as diabetic retinopathy, age-related macular degeneration (AMD), and glaucoma, is a significant factor driving the adoption of AI in ophthalmology. As the population ages, the incidence of these diseases increases, creating a need for efficient and accurate diagnostic tools. For instance, according to the CDC, the estimated number of Americans living with glaucoma in 2022 was 4.22 million. AI algorithms can rapidly analyze complex retinal images, facilitating early detection and treatment. For instance, AI systems have shown high sensitivity and specificity in identifying diabetic retinopathy, which allows for timely interventions and reduces the risk of vision loss.
Moreover, integrating advanced imaging techniques such as Optical Coherence Tomography (OCT) with AI has revolutionized ophthalmic diagnostics. High-resolution imaging provides detailed views of ocular structures, which enhances diagnostic precision when analyzed by artificial intelligence (AI). The availability of large datasets from these imaging technologies allows for the training of robust AI models, improving their accuracy and reliability in clinical settings. For instance, researchers at the Chinese University of Hong Kong (CUHK) have developed VisionFM, an advanced AI ophthalmic imaging foundation model. Trained on 3.4 million images across eight modalities, VisionFM diagnoses multiple eye diseases and uniquely predicts intracranial tumors from retinal images.
Furthermore, teleophthalmology, the remote delivery of eye care services, has gained traction, especially in underserved regions. AI is crucial in this expansion by enabling automated analysis of retinal images, facilitating remote diagnosis, and reducing the need for in-person consultations. This approach increases access to eye care and optimizes resource utilization in healthcare systems. For instance, in June 2024, C3 Med-Tech, an ophthalmic health tech startup, raised USD 0.23 million to launch AI-enabled, portable eye screening devices. The funding is expected to support telemedicine integration, real-time disease detection, and expansion across India, aiming to reduce avoidable blindness, especially in underserved communities facing a shortage of ophthalmologists.
Moreover, AI's ability to analyze and interpret data from Electronic Health Records (EHRs) facilitates personalized treatment plans in ophthalmology. AI predicts disease progression by assessing patient history, genetic information, and imaging data and recommends tailored interventions, further contributing to market growth.
Global AI In Ophthalmology Market Report Segmentation
This report forecasts revenue growth at global, regional, and country levels and provides an analysis of the latest industry trends in each of the sub-segments from 2018 to 2030. For this study, Grand View Research has segmented the global AI in ophthalmology market report based on application, deployment mode, technology, end-use, and region.
- Application Outlook (Revenue, USD Million, 2018 - 2030)
- Disease Detection and Monitoring
- Retinal Disease Detection
- Diabetic Retinopathy (DR)
- Diabetic Macular Edema (DME)
- Age-related Macular Degeneration (AMD)
- Retinal Vein Occlusion (RVO)
- Glaucoma Detection & Monitoring
- Surgical Planning & Outcome Prediction
- AI for Ophthalmic Imaging Workflow Automation
- Others
- Deployment Mode Outlook (Revenue, USD Million, 2018 - 2030)
- On Premise
- Cloud-based
- Technology Outlook (Revenue, USD Million, 2018 - 2030)
- Machine Learning
- Deep learning
- Supervised
- Unsupervised
- Others
- Natural Language Processing
- Clinical Documentation Assistance
- OCR (Optical Character Recognition)
- Auto-coding of Ophthalmology Notes
- Text Analytics for Diagnostic Reasoning
- Voice-based Diagnostic Recording (Speech-to-Text)
- Context-Aware Computing
- Computer Vision
- End-use Outlook (Revenue, USD Million, 2018 - 2030)
- Hospitals
- Specialty Ophthalmology Clinics
- Academic & Research Institutions
- Payers & Insurance Companies
- Others
- Regional Outlook (Revenue, USD Million, 2018 - 2030)
- North America
- Europe
- Germany
- UK
- France
- Italy
- Spain
- Denmark
- Sweden
- Norway
- Asia Pacific
- China
- Japan
- India
- South Korea
- Australia
- Thailand
- Latin America
- MEA
- South Africa
- Saudi Arabia
- UAE
- Kuwait
Table of Contents
Chapter 1. Methodology and Scope
- 1.1. Market Segmentation & Scope
- 1.2. Market Definitions
- 1.2.1. Application Segment
- 1.2.2. Deployment Mode Segment
- 1.2.3. Technology 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
- 1.6. 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. AI in Ophthalmology 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
- 3.2.1.1. Rising prevalence of eye diseases and diabetes
- 3.2.1.2. Advancements in imaging technology
- 3.2.1.3. Expansion of teleophthalmology services
- 3.2.2. Market restraint analysis
- 3.2.2.1. Data security and privacy concerns
- 3.2.2.2. High integration costs
- 3.2.3. Market opportunity analysis
- 3.2.4. Market challenges analysis
- 3.3. Case Studies
- 3.4. AI in Ophthalmology Market Analysis Tools
- 3.4.1. Industry Analysis - Porter's Five Forces Analysis
- 3.4.1.1. Supplier power
- 3.4.1.2. Buyer power
- 3.4.1.3. Substitution threat
- 3.4.1.4. Threat of new entrant
- 3.4.1.5. Competitive rivalry
- 3.4.2. PESTEL Analysis
- 3.4.2.1. Political landscape
- 3.4.2.2. Technological landscape
- 3.4.2.3. Economic landscape
- 3.4.2.4. Environmental Landscape
- 3.4.2.5. Legal Landscape
- 3.4.2.6. Social Landscape
Chapter 4. AI in Ophthalmology Market: Application Estimates & Trend Analysis
- 4.1. Segment Dashboard
- 4.2. Global AI in Ophthalmology Market Application Movement Analysis
- 4.3. Global AI in Ophthalmology Market Size & Trend Analysis, by Application, 2018 to 2030 (USD Million)
- 4.4. Disease Detection and Monitoring
- 4.4.1. Market estimates and forecasts 2018 to 2030 (USD Million)
- 4.4.2. Retinal Disease Detection
- 4.4.2.1. Market estimates and forecasts 2018 to 2030 (USD Million)
- 4.4.2.2. Diabetic Retinopathy (DR)
- 4.4.2.2.1. Market estimates and forecasts 2018 to 2030 (USD Million)
- 4.4.2.3. Diabetic Macular Edema (DME)
- 4.4.2.3.1. Market estimates and forecasts 2018 to 2030 (USD Million)
- 4.4.2.4. Age-related Macular Degeneration (AMD)
- 4.4.2.4.1. Market estimates and forecasts 2018 to 2030 (USD Million)
- 4.4.2.5. Retinal Vein Occlusion (RVO)
- 4.4.2.5.1. Market estimates and forecasts 2018 to 2030 (USD Million)
- 4.4.3. Glaucoma Detection & Monitoring
- 4.4.3.1. Market estimates and forecasts 2018 to 2030 (USD Million)
- 4.5. Surgical Planning & Outcome Prediction
- 4.5.1. Market estimates and forecasts 2018 to 2030 (USD Million)
- 4.6. AI for Ophthalmic Imaging Workflow Automation
- 4.6.1. Market estimates and forecasts 2018 to 2030 (USD Million)
- 4.7. Others
- 4.7.1. Market estimates and forecasts 2018 to 2030 (USD Million)
Chapter 5. AI in Ophthalmology Market: Deployment Mode Estimates & Trend Analysis
- 5.1. Segment Dashboard
- 5.2. Global AI in Ophthalmology Market Deployment Mode Movement Analysis
- 5.3. Global AI in Ophthalmology Market Size & Trend Analysis, by Deployment Mode, 2018 to 2030 (USD Million)
- 5.4. On Premise
- 5.4.1. Market estimates and forecasts 2018 to 2030 (USD Million)
- 5.5. Cloud-based
- 5.5.1. Market estimates and forecasts 2018 to 2030 (USD Million)
Chapter 6. AI in Ophthalmology Market: Technology Estimates & Trend Analysis
- 6.1. Segment Dashboard
- 6.2. Global AI in Ophthalmology Market Technology Movement Analysis
- 6.3. Global AI in Ophthalmology Market Size & Trend Analysis, by Technology, 2018 to 2030 (USD Million)
- 6.4. Machine Learning
- 6.4.1. Market estimates and forecasts 2018 to 2030 (USD Million)
- 6.4.2. Deep learning
- 6.4.2.1. Market estimates and forecasts 2018 to 2030 (USD Million)
- 6.4.3. Supervised
- 6.4.3.1. Market estimates and forecasts 2018 to 2030 (USD Million)
- 6.4.4. Unsupervised
- 6.4.4.1. Market estimates and forecasts 2018 to 2030 (USD Million)
- 6.4.5. Others
- 6.4.5.1. Market estimates and forecasts 2018 to 2030 (USD Million)
- 6.5. Natural Language Processing
- 6.5.1. Market estimates and forecasts 2018 to 2030 (USD Million)
- 6.5.2. Clinical Documentation Assistance
- 6.5.2.1. Market estimates and forecasts 2018 to 2030 (USD Million)
- 6.5.3. OCR (Optical Character Recognition)
- 6.5.3.1. Market estimates and forecasts 2018 to 2030 (USD Million)
- 6.5.4. Auto-coding of Ophthalmology Notes
- 6.5.4.1. Market estimates and forecasts 2018 to 2030 (USD Million)
- 6.5.5. Text Analytics for diagnostic reasoning
- 6.5.5.1. Market estimates and forecasts 2018 to 2030 (USD Million)
- 6.5.6. Voice-based Diagnostic Recording (Speech-to-Text)
- 6.5.6.1. Market estimates and forecasts 2018 to 2030 (USD Million)
- 6.6. Context-Aware Computing
- 6.6.1. Market estimates and forecasts 2018 to 2030 (USD Million)
- 6.7. Computer Vision
- 6.7.1. Market estimates and forecasts 2018 to 2030 (USD Million)
Chapter 7. AI in Ophthalmology Market: End Use Estimates & Trend Analysis
- 7.1. Segment Dashboard
- 7.2. Global AI in Ophthalmology Market End Use Movement Analysis
- 7.3. Global AI in Ophthalmology Market Size & Trend Analysis, by End Use, 2018 to 2030 (USD Million)
- 7.4. Hospitals
- 7.4.1. Market estimates and forecasts 2018 to 2030 (USD Million)
- 7.5. Specialty Ophthalmology Clinics
- 7.5.1. Market estimates and forecasts 2018 to 2030 (USD Million)
- 7.6. Academic & Research Institutions
- 7.6.1. Market estimates and forecasts 2018 to 2030 (USD Million)
- 7.7. Payers and Insurance Firms
- 7.7.1. Market estimates and forecasts 2018 to 2030 (USD Million)
- 7.8. Others
- 7.8.1. Market estimates and forecasts 2018 to 2030 (USD Million)
Chapter 8. AI in Ophthalmology Market: Regional Estimates & Trend Analysis
- 8.1. Regional Market Share Analysis, 2024 & 2030
- 8.2. Regional Market Dashboard
- 8.3. Market Size & Forecasts Trend Analysis, 2018 to 2030:
- 8.4. North America
- 8.4.1. U.S.
- 8.4.1.1. Key country dynamics
- 8.4.1.2. Regulatory framework
- 8.4.1.3. Competitive scenario
- 8.4.1.4. U.S. market estimates and forecasts 2018 to 2030 (USD Million)
- 8.4.2. Canada
- 8.4.2.1. Key country dynamics
- 8.4.2.2. Regulatory framework
- 8.4.2.3. Competitive scenario
- 8.4.2.4. Canada market estimates and forecasts 2018 to 2030 (USD Million)
- 8.4.3. Mexico
- 8.4.3.1. Key country dynamics
- 8.4.3.2. Regulatory framework
- 8.4.3.3. Competitive scenario
- 8.4.3.4. Mexico market estimates and forecasts 2018 to 2030 (USD Million)
- 8.5. Europe
- 8.5.1. UK
- 8.5.1.1. Key country dynamics
- 8.5.1.2. Regulatory framework
- 8.5.1.3. Competitive scenario
- 8.5.1.4. UK market estimates and forecasts 2018 to 2030 (USD Million)
- 8.5.2. Germany
- 8.5.2.1. Key country dynamics
- 8.5.2.2. Regulatory framework
- 8.5.2.3. Competitive scenario
- 8.5.2.4. Germany market estimates and forecasts 2018 to 2030 (USD Million)
- 8.5.3. France
- 8.5.3.1. Key country dynamics
- 8.5.3.2. Regulatory framework
- 8.5.3.3. Competitive scenario
- 8.5.3.4. France market estimates and forecasts 2018 to 2030 (USD Million)
- 8.5.4. Italy
- 8.5.4.1. Key country dynamics
- 8.5.4.2. Regulatory framework
- 8.5.4.3. Competitive scenario
- 8.5.4.4. Italy market estimates and forecasts 2018 to 2030 (USD Million)
- 8.5.5. Spain
- 8.5.5.1. Key country dynamics
- 8.5.5.2. Regulatory framework
- 8.5.5.3. Competitive scenario
- 8.5.5.4. Spain market estimates and forecasts 2018 to 2030 (USD Million)
- 8.5.6. Norway
- 8.5.6.1. Key country dynamics
- 8.5.6.2. Regulatory framework
- 8.5.6.3. Competitive scenario
- 8.5.6.4. Norway market estimates and forecasts 2018 to 2030 (USD Million)
- 8.5.7. Sweden
- 8.5.7.1. Key country dynamics
- 8.5.7.2. Regulatory framework
- 8.5.7.3. Competitive scenario
- 8.5.7.4. Sweden market estimates and forecasts 2018 to 2030 (USD Million)
- 8.5.8. Denmark
- 8.5.8.1. Key country dynamics
- 8.5.8.2. Regulatory framework
- 8.5.8.3. Competitive scenario
- 8.5.8.4. Denmark market estimates and forecasts 2018 to 2030 (USD Million)
- 8.6. Asia Pacific
- 8.6.1. Japan
- 8.6.1.1. Key country dynamics
- 8.6.1.2. Regulatory framework
- 8.6.1.3. Competitive scenario
- 8.6.1.4. Japan market estimates and forecasts 2018 to 2030 (USD Million)
- 8.6.2. China
- 8.6.2.1. Key country dynamics
- 8.6.2.2. Regulatory framework
- 8.6.2.3. Competitive scenario
- 8.6.2.4. China market estimates and forecasts 2018 to 2030 (USD Million)
- 8.6.3. India
- 8.6.3.1. Key country dynamics
- 8.6.3.2. Regulatory framework
- 8.6.3.3. Competitive scenario
- 8.6.3.4. India market estimates and forecasts 2018 to 2030 (USD Million)
- 8.6.4. Australia
- 8.6.4.1. Key country dynamics
- 8.6.4.2. Regulatory framework
- 8.6.4.3. Competitive scenario
- 8.6.4.4. Australia market estimates and forecasts 2018 to 2030 (USD Million)
- 8.6.5. South Korea
- 8.6.5.1. Key country dynamics
- 8.6.5.2. Regulatory framework
- 8.6.5.3. Competitive scenario
- 8.6.5.4. South Korea market estimates and forecasts 2018 to 2030 (USD Million)
- 8.6.6. Thailand
- 8.6.6.1. Key country dynamics
- 8.6.6.2. Regulatory framework
- 8.6.6.3. Competitive scenario
- 8.6.6.4. Thailand market estimates and forecasts 2018 to 2030 (USD Million)
- 8.7. Latin America
- 8.7.1. Brazil
- 8.7.1.1. Key country dynamics
- 8.7.1.2. Regulatory framework
- 8.7.1.3. Competitive scenario
- 8.7.1.4. Brazil market estimates and forecasts 2018 to 2030 (USD Million)
- 8.7.2. Argentina
- 8.7.2.1. Key country dynamics
- 8.7.2.2. Regulatory framework
- 8.7.2.3. Competitive scenario
- 8.7.2.4. Argentina market estimates and forecasts 2018 to 2030 (USD Million)
- 8.8. MEA
- 8.8.1. South Africa
- 8.8.1.1. Key country dynamics
- 8.8.1.2. Regulatory framework
- 8.8.1.3. Competitive scenario
- 8.8.1.4. South Africa market estimates and forecasts 2018 to 2030 (USD Million)
- 8.8.2. Saudi Arabia
- 8.8.2.1. Key country dynamics
- 8.8.2.2. Regulatory framework
- 8.8.2.3. Competitive scenario
- 8.8.2.4. Saudi Arabia market estimates and forecasts 2018 to 2030 (USD Million)
- 8.8.3. UAE
- 8.8.3.1. Key country dynamics
- 8.8.3.2. Regulatory framework
- 8.8.3.3. Competitive scenario
- 8.8.3.4. UAE market estimates and forecasts 2018 to 2030 (USD Million)
- 8.8.4. Kuwait
- 8.8.4.1. Key country dynamics
- 8.8.4.2. Regulatory framework
- 8.8.4.3. Competitive scenario
- 8.8.4.4. Kuwait market estimates and forecasts 2018 to 2030 (USD Million)
Chapter 9. Competitive Landscape
- 9.1. Company/Competition Categorization
- 9.2. Strategy Mapping
- 9.3. Company Market Position Analysis, 2024
- 9.4. Company Profiles/Listing
- 9.4.1. OphtAI
- 9.4.1.1. Company overview
- 9.4.1.2. Financial performance
- 9.4.1.3. Technology type benchmarking
- 9.4.1.4. Strategic initiatives
- 9.4.2. Eyenuk, Inc.
- 9.4.2.1. Company overview
- 9.4.2.2. Financial performance
- 9.4.2.3. Technology type benchmarking
- 9.4.2.4. Strategic initiatives
- 9.4.3. Google LLC
- 9.4.3.1. Company overview
- 9.4.3.2. Financial performance
- 9.4.3.3. Technology type benchmarking
- 9.4.3.4. Strategic initiatives
- 9.4.4. IBM Corporation
- 9.4.4.1. Company overview
- 9.4.4.2. Financial performance
- 9.4.4.3. Technology type benchmarking
- 9.4.4.4. Strategic initiatives
- 9.4.5. Optos plc
- 9.4.5.1. Company overview
- 9.4.5.2. Financial performance
- 9.4.5.3. Technology type benchmarking
- 9.4.5.4. Strategic initiatives
- 9.4.6. Zeiss
- 9.4.6.1. Company overview
- 9.4.6.2. Financial performance
- 9.4.6.3. Technology type benchmarking
- 9.4.6.4. Strategic initiatives
- 9.4.7. Topcon Healthcare
- 9.4.7.1. Company overview
- 9.4.7.2. Financial performance
- 9.4.7.3. Technology type benchmarking
- 9.4.7.4. Strategic initiatives
- 9.4.8. Ikerian AG (RetinAi)
- 9.4.8.1. Company overview
- 9.4.8.2. Financial performance
- 9.4.8.3. Technology type benchmarking
- 9.4.8.4. Strategic initiatives
- 9.4.9. Nidek Co., Ltd.
- 9.4.9.1. Company overview
- 9.4.9.2. Financial performance
- 9.4.9.3. Technology type benchmarking
- 9.4.9.4. Strategic initiatives
- 9.4.10. Altris AI
- 9.4.10.1. Company overview
- 9.4.10.2. Financial performance
- 9.4.10.3. Technology type benchmarking
- 9.4.10.4. Strategic initiatives
- 9.4.11. Remidio Innovative Solutions Pvt Ltd.
- 9.4.11.1. Company overview
- 9.4.11.2. Financial performance
- 9.4.11.3. Technology type benchmarking
- 9.4.11.4. Strategic initiatives
- 9.4.12. Oculus Maxima LIMITED
- 9.4.12.1. Company overview
- 9.4.12.2. Financial performance
- 9.4.12.3. Technology type benchmarking
- 9.4.12.4. Strategic initiatives
- 9.4.13. Siemens Healthineers
- 9.4.13.1. Company overview
- 9.4.13.2. Financial performance
- 9.4.13.3. Technology type benchmarking
- 9.4.13.4. Strategic initiatives
- 9.4.14. Haag-Streit Group
- 9.4.14.1. Company overview
- 9.4.14.2. Financial performance
- 9.4.14.3. Technology type benchmarking
- 9.4.14.4. Strategic initiatives
