![]() |
市场调查报告书
商品编码
1968486
人工智慧驱动的视网膜影像分析市场-全球产业规模、份额、趋势、机会、预测:按类型、应用、地区和竞争格局划分,2021-2031年AI Powered Retina Image Analysis Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Type, By Application, By Region & Competition, 2021-2031F |
||||||
全球人工智慧驱动的视网膜成像市场预计将从 2025 年的 11.5 亿美元成长到 2031 年的 17.3 亿美元,复合年增长率为 7.04%。
该市场涵盖用于数位眼底影像分析的机器学习软体,旨在实现眼科疾病的自动检测。主要成长要素包括需要频繁眼科检查的慢性疾病盛行率不断上升,以及由于全球眼科医师短缺而迫切需要加快诊断速度。根据国际糖尿病联盟预测,到2024年,全球将有约5.89亿成年人患有糖尿病,显示迫切需要可扩展的筛检工具来追踪视网膜健康状况。
| 市场概览 | |
|---|---|
| 预测期 | 2027-2031 |
| 市场规模:2025年 | 11.5亿美元 |
| 市场规模:2031年 | 17.3亿美元 |
| 复合年增长率:2026-2031年 | 7.04% |
| 成长最快的细分市场 | 糖尿病视网膜病变的检测 |
| 最大的市场 | 北美洲 |
阻碍市场扩张的主要障碍是医疗人工智慧检验方面严格的监管环境。製造商必须遵守复杂的核准程序,需要提供强有力的临床证据来证明其演算法在不同患者群体和硬体系统上的准确性。这种监管负担构成了重大的准入门槛,并减缓了自动化诊断工具在常规医疗实践中的应用。
糖尿病视网膜病变和老龄化黄斑部病变的发生率不断上升,是全球人工智慧驱动的视网膜成像分析市场的主要驱动力。随着全球糖尿病患者人数的不断增长,医疗保健系统面临着常规视网膜筛检的巨大需求,而眼科医师的手动评估已无法充分应对这项挑战。因此,迫切需要可扩展的、人工智慧驱动的解决方案来有效地筛选患者。根据美国疾病管制与预防中心 (CDC) 于 2024 年 5 月发布的 VEHSS 模型估计值报告,美国约有 960 万人患有糖尿病视网膜病变,凸显了製定有效筛检通讯协定以预防视力丧失的紧迫性。
同时,越来越多的监管机构核准基于人工智慧的医疗软体,降低了进入门槛,并证实了这些技术的临床效用。监管机构正在为自主诊断制定明确的路径,并鼓励製造商将适用于桌上型和携带式成像设备的创新技术商业化。例如,2024年4月,AEYE Health宣布其首个使用携带式眼底摄影机影像诊断糖尿病视网膜病变的完全自主人工智慧解决方案获得了FDA核准,该解决方案可帮助患者转院治疗。这项监管进展得到了强劲资金筹措的支持。 2024年,《眼科时报》报道称,Mediwhale已筹集1,200万美元,用于开发人工智慧驱动的视网膜扫描技术,以预测心血管疾病。
医疗人工智慧有效性检验的严格监管环境是全球人工智慧驱动的视网膜成像分析市场扩张的主要障碍。企业必须提供大量的临床证据,证明其演算法在不同的患者群体和成像设备上都能保持高精度。这种全面的检验要求造成了巨大的财务和营运壁垒,常常阻碍小规模的创新者将解决方案推向市场,并延缓了先进诊断工具的发布。
这些监管障碍直接导致产品商业化和市场渗透速度放缓。近期行业数据显示,获得审批的难度显而易见。根据美国眼科学会 (ARVO) 2024 年的调查,自 2016 年以来开发的 47 种眼科人工智慧演算法中,仅有 26 种获得监管部门核准。这一巨大差距凸显了製造商在遵守监管环境方面面临的困难,从而限制了可用自动化筛检技术的供应,并阻碍了整体市场成长。
眼科领域基础模型和生成式人工智慧的兴起,标誌着模式转移,从针对特定任务的专用演算法转向能够处理各种临床场景的通用型自监督系统。与基于有限标註资料集训练的传统模型不同,这些基础模型利用庞大的未标註视网膜影像库来学习通用特征。这显着减轻了数据标註的负担,并提高了对不同人群和硬体环境的适应性。这项技术进步使得即使在影像品质波动较大的真实临床环境中,也能实现卓越的诊断效能。例如,根据2025年6月《眼科医生》杂誌的报道,RETFound基础模型应用于社区医疗保健的眼底疾病筛检时,其敏感度和特异性比标准商业人工智慧工具提高了15%以上。
同时,市场正迅速向系统性疾病生物标记的检测领域拓展,将视网膜成像转变为一种非侵入性的生理健康监测手段,其应用范围已超越眼科疾病。开发人员正利用深度学习技术,增强识别视网膜中与神经退化性疾病相关的微小微血管和神经变化的能力,从而实现对以往难以诊断的疾病的早期疗育。这项技术透过将视网膜分析平台的效用扩展到基层医疗和神经病学领域,创造了新的价值提案。为了凸显这一进展,PMLiVE于2025年7月报告称,其新开发的深度学习框架「Eye-AD」成功分析了视网膜血管结构,并以0.9355的AUC值检测出早期阿兹海默症。
The Global AI Powered Retina Image Analysis Market is projected to increase from USD 1.15 Billion in 2025 to USD 1.73 Billion by 2031, reflecting a CAGR of 7.04%. This market encompasses machine learning software developed to analyze digital fundus images for the automated detection of ocular diseases. Primary growth drivers include the rising burden of chronic illnesses that necessitate frequent eye exams and the urgent requirement to improve diagnostic speed due to a worldwide shortage of ophthalmologists. According to the International Diabetes Federation, roughly 589 million adults were living with diabetes in 2024, highlighting the critical need for scalable screening tools to track retinal health.
| Market Overview | |
|---|---|
| Forecast Period | 2027-2031 |
| Market Size 2025 | USD 1.15 Billion |
| Market Size 2031 | USD 1.73 Billion |
| CAGR 2026-2031 | 7.04% |
| Fastest Growing Segment | Diabetic Retinopathy Detection |
| Largest Market | North America |
A major obstacle hindering market expansion is the rigorous regulatory landscape regarding the validation of medical artificial intelligence. Manufacturers must navigate complicated approval procedures that require robust clinical evidence to demonstrate algorithm precision across diverse patient populations and hardware systems. This regulatory weight establishes significant barriers to entry and delays the incorporation of automated diagnostic tools into routine medical practice.
Market Driver
The growing incidence of diabetic retinopathy and age-related macular degeneration acts as a primary catalyst for the Global AI Powered Retina Image Analysis Market. As the global diabetic population increases, healthcare systems struggle with an excessive demand for regular retinal screenings that manual assessment by ophthalmologists cannot support. This burden creates a pressing need for scalable, AI-driven solutions capable of triaging patients efficiently. As noted in the 'VEHSS Modeled Estimates' report by the Centers for Disease Control and Prevention in May 2024, an estimated 9.6 million individuals in the United States were living with diabetic retinopathy, emphasizing the vital need for effective screening protocols to prevent vision loss.
Concurrently, rising regulatory approvals for AI-based medical software are lowering entry barriers and confirming the clinical utility of these technologies. Regulators are defining clearer routes for autonomous diagnostics, encouraging manufacturers to commercialize innovations that function with both tabletop and portable imaging devices. For example, AEYE Health announced in April 2024 that it received the first FDA clearance for a fully autonomous AI solution designed to diagnose referable diabetic retinopathy using handheld fundus camera images. This regulatory momentum is supported by strong investment; Ophthalmology Times reported in 2024 that Mediwhale secured $12 million to advance its AI-powered retina scan technology for cardiovascular disease prediction.
Market Challenge
The strict regulatory environment governing the validation of medical artificial intelligence serves as a major obstruction to the expansion of the Global AI Powered Retina Image Analysis Market. Companies face rigorous requirements to provide extensive clinical evidence proving that their algorithms maintain high accuracy across varied patient demographics and imaging hardware. This necessity for comprehensive validation creates substantial financial and operational entry barriers, often preventing smaller innovators from launching their solutions and delaying the release of advanced diagnostic tools.
These regulatory hurdles directly correlate with a slower rate of product commercialization and market penetration. The challenge of securing authorization is evident in recent sector data. According to the Association for Research in Vision and Ophthalmology, research in 2024 indicated that only 26 ophthalmology AI algorithms had successfully achieved regulatory approval out of 47 developed since 2016. This significant gap underscores the difficulty manufacturers face in navigating compliance landscapes, which ultimately restricts the volume of available automated screening technologies and hampers the overall growth trajectory of the market.
Market Trends
The rise of ophthalmic foundation models and generative AI marks a paradigm shift from task-specific algorithms to versatile, self-supervised systems capable of handling diverse clinical scenarios. Unlike traditional models trained on limited labeled datasets, these foundation models utilize vast repositories of unlabeled retinal images to learn generalizable features, significantly reducing the data annotation burden and improving adaptability across different demographics and hardware. This technological advancement enables superior diagnostic performance in real-world settings where image quality varies. For example, according to The Ophthalmologist in June 2025, the RETFound foundation model demonstrated greater than 15 percent better sensitivity and specificity compared to standard commercial AI tools when applied to community-based fundus disease screenings.
Simultaneously, the market is witnessing a rapid expansion into systemic disease biomarker detection, effectively transforming retinal imaging into a non-invasive window for monitoring physiological health beyond ocular pathologies. Developers are increasingly leveraging deep learning to identify subtle microvascular and neuronal changes in the retina that correlate with neurodegenerative conditions, facilitating earlier intervention for diseases that are traditionally difficult to diagnose. This capability is creating new value propositions for retinal analysis platforms by extending their utility into primary care and neurology sectors. Highlighting this progress, PMLiVE reported in July 2025 that the newly developed Eye-AD deep learning framework successfully analyzed retinal vasculature to detect early-onset Alzheimer's disease with an AUC of 0.9355.
Report Scope
In this report, the Global AI Powered Retina Image Analysis Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:
Company Profiles: Detailed analysis of the major companies present in the Global AI Powered Retina Image Analysis Market.
Global AI Powered Retina Image Analysis Market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report: