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

AI In Ophthalmology Market Summary

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. 

Recent Partnerships & Collaborations in the AI In Ophthalmology Industry

Recent Product Launches in the AI in Ophthalmology Industry

Market Concentration & Characteristics

The chart below illustrates the relationship between industry concentration, industry characteristics, and industry participants. The x-axis represents the level of industry concentration, ranging from low to high. The y-axis represents various industry characteristics, including industry competition, level of partnerships & collaboration activities, degree of innovation, impact of regulations, and regional expansion. The artificial intelligence (AI) in ophthalmology industry is slightly fragmented, with the presence of several emerging solution providers dominating the market. The degree of innovation is high. The level of merger & acquisition activities is moderate. Moreover, the regional expansion and impact of regulations on industry are high.

The AI in ophthalmology industry experiences a high degree of innovation driven by technological advancements. The increasing adoption of artificial intelligence in clinical diagnostics, patient engagement, surgical planning, clinical documentation, and hospital administration supports new innovations in the market. For instance, in February 2025, Telefónica developed an innovative healthcare solution using 5G, edge computing, and AI: "CatEye," a portable device that autonomously captures eye images to assess cataract severity and surgical need.

The industry is experiencing a moderate level of merger and acquisition activities undertaken by several key players. This is due to the desire to gain a competitive advantage in the industry, enhance technological capabilities, and consolidate in a rapidly growing market.  

AI applications in ophthalmology are often categorized based on their risk levels. High-risk applications, such as diagnostic tools for conditions such as diabetic retinopathy, require stringent regulatory oversight. The deployment of AI in ophthalmology involves handling sensitive patient data. Regulations such as the Health Insurance Portability and Accountability Act (HIPAA) in the U.S. and the General Data Protection Regulation (GDPR) in the EU mandate strict data privacy and security measures. AI systems must ensure data anonymization, secure storage, and controlled access to protect patient confidentiality and trust in digital health solutions.

Companies within the artificial intelligence (AI) in ophthalmology sector seek geographic expansion strategies to maintain their foothold in emerging markets and attract customers from these regions.The industry is witnessing high geographical expansion, driven by an increasing customer base for AI in ophthalmology. With the growing adoption of digital healthcare solutions, the market is expected to grow significantly in the coming years, especially in developing countries. For instance, in October 2024, Google licensed its AI model for detecting diabetic retinopathy to partners in India and Thailand, addressing the shortage of eye specialists in these countries. Collaborating with Aravind Eye Hospital and Rajavithi Hospital, the initiative aims to improve access to eye care, targeting six million AI-enabled screenings over the next decade.

"We’ve also been working with the Thai Ministry of Public Health’s Department of Medical Services (DMS), which is responsible for the country’s diabetic retinopathy screening program, on implementation research and cost-effectiveness analysis. This collaboration is bringing our AI into Thailand’s National Innovation program and will pave the way for a partnership between Perceptra and DMS to apply the diabetic retinopathy AI model in public sector hospitals and help deliver impact at the population scale."

-Google Blog Post

Case Study Insights: Enhancing Diabetic Retinopathy Screening with the Aireen AI System

Introduction

Diabetic retinopathy (DR) is a leading cause of vision impairment among individuals with diabetes. Early detection through regular screening is crucial to prevent progression to severe stages. However, traditional screening methods often face challenges such as limited access to specialists, especially in remote areas, and variability in diagnostic accuracy. The integration of artificial intelligence (AI) in ophthalmology presents an opportunity to address these challenges by providing efficient and accurate screening solutions.

Challenges

The primary challenges in DR screening include:

• Limited Access to Specialists: Many regions lack sufficient numbers of ophthalmologists, leading to delays in diagnosis and treatment.

• Variability in Diagnostic Accuracy: The interpretation of fundus images can vary between general ophthalmologists and retina specialists, potentially affecting the consistency of DR detection.

• Resource Constraints: Implementing widespread screening programs is resource-intensive and requires significant time and financial investment.

Solution

To address these challenges, a real-world study was conducted to evaluate the effectiveness of the Aireen AI system in DR screening. The study involved 1,274 patients with type I or II diabetes, each undergoing one-field fundus photography using a non-mydriatic camera. The images were independently assessed by:

1. General ophthalmologists without subspecialty training in retina (GO).

2. Retina specialists (RS).

3. The Aireen AI system.

Discrepancies among these assessments were resolved by a Diabetic Retinopathy Board (DRB), serving as the clinical reference standard.

Results/Outcomes

The study found that the Aireen AI system demonstrated superior performance in detecting DR compared to GO and RS assessments.

• Sensitivity: Aireen achieved a sensitivity of 92.1%, outperforming GO (87.0%) and RS (82.9%).

• Specificity: Aireen's specificity was 90.7%, higher than GO (76.5%) and RS (81.2%).

These results indicate that the Aireen AI system exceeds the diagnostic accuracy of human specialists in DR screening. Its implementation is expected to lead to more consistent and efficient screening processes, especially in areas with limited access to specialized care.

Conclusion

The integration of the Aireen AI system into DR screening protocols offers a promising solution to existing challenges in ophthalmic care. By providing high sensitivity and specificity in detecting DR, the system can enhance early diagnosis, streamline screening processes, and potentially reduce the burden on healthcare resources. This study underscores the potential of AI-driven tools in transforming ophthalmology and improving patient outcomes.

Application Insights

Based on application, the disease detection and monitoring segment held the largest market share of over 60% in 2024. The adoption of AI-powered diagnostic tools has enabled autonomous or semi-autonomous detection of retinal diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma, among others, with high sensitivity and specificity. These tools are increasingly deployed in primary care clinics, optometry chains, and teleophthalmology platforms, facilitating earlier detection and referral where specialist availability is limited. Furthermore, this segment benefits from growing support through reimbursement frameworks in developed countries, where AI-enabled diagnostic solutions have been approved for clinical use. Such factors contribute to the segment’s growth.

The surgical planning segment is expected to grow at the fastest CAGR during the forecast period, driven by advancements in precision medicine, intraoperative imaging integration, and personalized surgical decision-making. AI is increasingly utilized to enhance preoperative planning in procedures such as cataract extraction, refractive correction, and vitreoretinal surgeries, offering capabilities such as automated biometric analysis, IOL power prediction, and corneal mapping. These AI-driven insights support ophthalmic surgeons in tailoring procedures to individual patient anatomy and disease profiles, thus contributing to the segment’s growth further.

Deployment Mode Insights

Based on deployment mode, the cloud-based segment held the largest revenue share of over 85% in 2024. Cloud-based AI solutions are gaining popularity for their scalability, ease of access, and seamless updates. These platforms allow AI algorithms to analyze imaging data remotely, enabling broader reach, especially for teleophthalmology and screening programs in rural or low-resource settings. For instance, Eyenuk’s EyeArt is a U.S. FDA-cleared autonomous AI system for diabetic retinopathy screening that operates as a cloud-based service, allowing clinics to upload fundus images for instant analysis without needing local servers. In addition, Google’s DeepMind collaborated with Moorfields Eye Hospital in the UK to develop cloud-based OCT interpretation tools.

On-premise deployment is preferred by large hospitals and academic centers that require data sovereignty, low-latency processing, and integration with internal IT systems. Institutions such as Aravind Eye Hospital (India) and Bascom Palmer Eye Institute (U.S.) have implemented AI solutions locally to process large imaging volumes while maintaining control over sensitive patient data. These deployments are particularly suited for AI use cases in surgical planning, multimodal imaging, and clinical trials, where real-time decision-making and data confidentiality are critical.

Technology Insights

Based on technology, the machine learning segment held the largest revenue share of over 35% in 2024 due to its efficacy in automating complex image analysis tasks integral to ophthalmic diagnostics. Ophthalmology is an image-dependent specialty that relies heavily on modalities such as optical coherence tomography (OCT) and fundus photography. Machine learning algorithms process large volumes of imaging data, facilitating rapid and accurate detection and classification of diseases such as diabetic retinopathy, glaucoma, and age-related macular degeneration. In addition, integrating ML with electronic health records (EHR) and clinical workflows improves operational efficiency and patient management, further contributing to segment growth.

The context-aware computing segment is expected to grow at the fastest CAGR from 2025 to 2030. Unlike traditional AI models, context-aware systems synthesize multiple data streams, including electronic health records (EHRs), real-time vital signs, patient medical histories, environmental factors, and lifestyle information, to develop a comprehensive understanding of patient health in real time. This enables personalized diagnostics and treatment recommendations, improving clinical outcomes and enabling proactive disease management.

End-use Insights

Based on end use, the hospitals segment held the largest market share of over 37% in 2024 due to its robust infrastructure, high imaging volumes, and integration capabilities required to support AI-enabled diagnostic and clinical decision-support tools. In addition, hospitals are equipped with electronic medical records (EMR) systems and Picture Archiving and Communication Systems (PACS), allowing for seamless integration of AI solutions into their clinical workflows. Furthermore, hospitals frequently participate in clinical trials and collaborate with academic institutions, making them ideal settings for testing and validating new AI technologies.

The specialty ophthalmology clinics segment is anticipated to grow at the fastest CAGR from 2025 to 2030, driven by growing demand for outpatient eye care, the proliferation of diagnostic imaging, and the rising availability of plug-and-play AI solutions tailored for smaller practices. These clinics are increasingly adopting AI-based screening and diagnostic tools for conditions such as diabetic retinopathy, macular degeneration, and glaucoma to improve efficiency and enable early disease detection. 

Regional Insights

North America AI in ophthalmology industry held the largest revenue share of over 54% in 2024. This is attributed to the robust healthcare infrastructure, widespread adoption of AI/ML technologies, lucrative funding options, and the presence of several key players. Moreover, favorable government initiatives and a strong reimbursement framework propel market growth further.

U.S. AI In Ophthalmology Market Trends

The AI in ophthalmology industry in the U.S. held the largest market share in 2024 due to the high prevalence of diabetic retinopathy and glaucoma, robust regulatory and reimbursement framework, and strong technology and health ecosystem. Moreover, key players are undertaking strategic initiatives such as new product launches, partnerships, collaborations, mergers and acquisitions, etc., to enhance their market presence. For instance, in May 2024, Optomed launched the Optomed Aurora AEYE in the U.S. This handheld AI fundus camera is designed to autonomously detect more-than-mild diabetic retinopathy.

Europe AI In Ophthalmology Market Trends

The AI in ophthalmology industry in Europe is expected to witness significant growth during the forecast period. This is attributed to the widespread adoption of AI technologies in healthcare, increasing investments by government and private organizations, and the growing geriatric population. Moreover, the region also benefits from robust ophthalmology imaging penetration and the emergence of AI startups, thus contributing to further market growth.

The AI in ophthalmology industry in the UK is expected to grow over the forecast period, owing to the robust healthcare infrastructure and increasing investments in AI technologies to enhance patient care, optimize operations, and tackle various healthcare challenges. In addition, supportive government initiatives propel market growth further.  

Germany AI in ophthalmology industry held the largest market share in 2024 in the European market, attributed to increasing investments in healthcare technology and robust healthcare infrastructure. Moreover, the growing geriatric population and high disease burden, advanced imaging infrastructure, and robust research and development in healthcare are some other factors contributing to market growth. 

Asia Pacific AI In Ophthalmology Market Trends

Asia Pacific AI in ophthalmology industry is projected to experience fastest growth in the coming years. The rising prevalence of diabetes, eye diseases, and growing geriatric population are major factors contributing to market growth. Moreover, increasing healthcare expenditure and growing adoption of artificial intelligence in healthcare propel market growth further. 

Japan AI in ophthalmology industryis expected to grow significantly over the forecast period. The growing adoption of artificial intelligence in healthcare and strong academy-industry collaboration contribute to market growth. In addition, technological advancements and growing government support and initiatives further fuel market growth.

AI in ophthalmology industry in India is expected to grow rapidly, owing to the increasing healthcare expenditure and rapid adoption of healthcare IT solutions. Moreover, the rising prevalence of diabetes and eye health diseases propel market growth further. In addition, supportive government initiatives are expected to propel market growth further. For instance, in February 2025, the Government of Kerala partnered with Remidio to launch Nayanamritham 2.0, an AI-driven government screening program for chronic eye diseases. This initiative enhances early detection of diabetic retinopathy, glaucoma, and age-related macular degeneration, utilizing AI-enabled fundus cameras to improve accessibility and efficiency in eye care.

“AI isn’t here to replace healthcare providers-it’s here to empower them. By leveraging ethical AI, we are enabling optometrists to detect routine cases with AI-powered cameras, while ophthalmologists can focus on advanced cases, delivering the highest quality care where it’s needed most.” 

-Dr Bipin Gopal, Deputy Director, DHS, Government of Kerala

Latin America AI In Ophthalmology Market Trends

Latin America AI in ophthalmology industry is anticipated to grow at a significant CAGR over the forecast period. This is attributed to the growing awareness about AI technologies, increasing government spending, and growing advancements in healthcare infrastructure.

Middle East and Africa AI In Ophthalmology Market Trends

Middle East and Africa AI in ophthalmology industry is expected to grow at a significant CAGR over the forecast period. The market is characterized by a dynamic landscape driven by the growing adoption of healthcare IT solutions, increasing healthcare expenditures, and supportive government policies. Significant integration of AI in healthcare technology across the region contributes to market growth further. For instance, the UAE and Saudi Arabia governments are investing heavily in AI-driven healthcare infrastructure under initiatives like Saudi Vision 2030 and the UAE's National AI Strategy.

Saudi Arabia AI in ophthalmology industry is expected to grow rapidly over the forecast period, owing to strong government initiatives and technological advancements. For instance, the Saudi government's increasing focus on digital transformation, as part of Saudi Vision 2030, is a significant driver for adopting AI technologies in healthcare. In addition, strategic partnerships with global AI companies and increasing product innovations contribute to market growth further. For instance, in October 2023, Eyenai, Saudi Arabia's first locally developed AI-powered ophthalmic solution, was launched to revolutionize diabetic retinopathy screening. A consortium including SDAIA, King Khalid Eye Specialist Hospital, Lean Business Services, and SCAI unveiled this solution.

Key AI In Ophthalmology Company Insights

Key players operating in the AI in ophthalmology industry are undertaking various initiatives to strengthen their market presence and increase the reach of their products and services. Strategies such as new product launches and partnerships are playing a key role in propelling market growth.

Recent Developments

• In May 2025, The Alliance for Healthcare from the Eye was launched at the Association for Research in Vision and Ophthalmology (ARVO) to enhance disease detection and coordinated care using oculomics and AI. This initiative aims to leverage ocular data for proactive healthcare, improving access and outcomes while ensuring patient privacy. It involves collaboration among health systems, clinicians, and industry innovators in the U.S.

“The eye offers a non-invasive, high-resolution window into the body’s vascular, neurologic, and metabolic systems. With advanced ophthalmic diagnostics, AI can help identify early indicators of heart disease, kidney dysfunction, neurodegeneration and other systemic diseases-before symptoms arise.”

-Robert N. Weinreb, MD, Professor and Chair, Ophthalmology at the University of California, San Diego.

• In October 2024, Mount Sinai’s Icahn School of Medicine received a USD 5 million gift from the John and Daria Barry Foundation to expand its Center for Ophthalmic Artificial Intelligence and Human Health. The funding is anticipated to enhance AI-driven eye disease research, early diagnosis, clinical programs, fellowships, and equitable, primary care-based vision care initiatives.

“By supporting Mount Sinai’s Center for Ophthalmic AI and Human Health, we aim to enable earlier diagnoses and more effective treatments while also ensuring that cutting-edge technology is accessible to all. We are proud to be part of this transformative initiative and confident it will push the boundaries of what’s possible in both ophthalmology and broader human health.” 

- Daria Barry

• In February 2024, RetinAI launched RetinAI Discovery for Clinics, offering two versions tailored for ophthalmologists and optometrists. This cloud-based, vendor-neutral platform harmonizes diverse imaging data, using AI algorithms to enhance clinical decision workflows, teleophthalmology, referrals, and patient management. 

• In December 2022, RetinAI Medical AG launched Discovery CORE, an AI-powered platform designed to accelerate clinical and academic ophthalmology research by enabling real-time collaboration on medical imaging datasets. It automatically measures retinal fluid volumes and layer thickness from OCT scans, supports annotation and case report forms, and facilitates large-scale disease endpoint assessment and registry building.

AI In Ophthalmology Market Report Scope

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

    • U.S.

    • Canada

    • Mexico

  • Europe

    • Germany

    • UK

    • France

    • Italy

    • Spain

    • Denmark

    • Sweden

    • Norway

  • Asia Pacific

    • China

    • Japan

    • India

    • South Korea

    • Australia

    • Thailand

  • Latin America

    • Brazil

    • Argentina

  • MEA

    • South Africa

    • Saudi Arabia

    • UAE

    • Kuwait