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Eyenuk

by Eyenuk, Inc.  ·  US

Last verified

EyeArt is FDA-cleared autonomous AI that grades diabetic retinopathy from retinal photos, with no physician overread needed.

Visit Eyenuk (opens in new tab)Read our methodology

At a glance

Pricing
No public price.
HIPAA
Attested
SOC 2
Type II
EHRs
—
Founded
—
HQ
US
On this page

Independent score  ·  By our public rubric

71/100Top-ranked

Editorial rating 5.0 / 5

How it’s computed →
  • Regulatory & Compliance
    28/28

    FDA cleared (2 K-numbers on record)

  • Clinical Integration
    0/6.6

    No EHR integrations listed

  • Evidence Strength
    26/26

    5 peer-reviewed papers

  • Vendor & Market
    3/18

    Seed-stage or unfunded

  • Sentiment & Transparency
    2.5/5

    Pricing on request, no public amounts

▸ Show all 11 dimensions

Regulatory & Compliance

  • FDA clearance18/18

    FDA cleared (2 K-numbers on record)

  • HIPAA / SOC2 / BAA10/10

    HIPAA + (SOC2 or BAA) attested

Clinical Integration

  • EHR integrations (count)0/4

    No EHR integrations listed

  • Top-3 EHR coverage (Epic / Oracle / Athena)0/2

    None of the top-3 EHRs covered

Evidence Strength

  • Peer-reviewed papers18/18

    5 peer-reviewed papers

  • RCT / meta-analysis / systematic review8/8

    1 RCT/Meta-Analysis/Systematic Review

Vendor & Market

  • Funding & adoption signal3/12

    Seed-stage or unfunded

  • Years in market0/6

    Founded year not recorded

Sentiment & Transparency

  • Pricing transparency3/5

    Pricing on request, no public amounts

Scored November 2, 2026 · Public scoring method v1.2.0

EyeArt is FDA-cleared autonomous AI that grades diabetic retinopathy from retinal photos, with no physician overread needed.

No public price. HIPAA + SOC 2 Type II attested.

Evidence record

FDA decisions
Peer-reviewed

5 studies on PubMed: 3 other, 1 meta-analysis, 1 observational, published 2024 to 2026. Read the literature summary

Verification
  • Score computed November 2, 2026, method 1.2.0
  • Review written November 2, 2026
Cite this record

MedAI Verdict, Eyenuk record, https://medaiverdict.com/tools/eyenuk, version November 2, 2026

Change history
View every change to this record

Bottom line

EyeArt, from Eyenuk, Inc., is retinal photo software that Eyenuk says grades diabetic retinopathy autonomously, with no physician overread. FDA's records list two EyeArt clearances, K200667 in August 2020 and K223357 in June 2023, and the K numbers found cover diabetic retinopathy. Eyenuk calls EyeArt a multidisease platform expanding to AMD and glaucoma. A primary care clinic, health system or screening program checking adults with diabetes who have never had a retinopathy diagnosis is the target user. AMD and glaucoma reads sit among the indications the vendor describes as expanding.

What does the evidence cover?

Eyenuk's 2023 press release reports 94.4% sensitivity and 91.1% specificity for more than mild retinopathy, and 96.8% and 91.6% for vision threatening disease, from the prospective multicenter trial NCT04984200. The vendor cites a 2021 JAMA Network Open paper (PMID 34779843) on autonomous detection of referable and vision threatening retinopathy as its main evaluation, and the sponsor is unverified.

A 2021 British Journal of Ophthalmology paper (PMID 32606081) evaluated AI screening prospectively in 30,000 patients in an English screening setting. Two 2026 systematic reviews with meta-analyses, in Ophthalmologica and the American Journal of Ophthalmology, pool EyeArt's diagnostic accuracy. Eyenuk's studies page lists six peer reviewed studies, one of them a November 2025 triage grading study in the English national program, without marking which are independent.

What does EyeArt do in practice?

The intended population is adults with diabetes not previously diagnosed with retinopathy. Eyenuk says over 90% get conclusive results without dilation and that the software gives the operator real time image quality feedback. The input is color fundus photography of the retina only. The vendor lists the Canon CR-2 AF and CR-2 Plus AF cameras, and its June 2023 release adds the Topcon NW400.

Eyenuk says EyeArt orders and reports flow through "multiple major EHR systems, including EPIC". It states HIPAA, SOC 2 Type 2, GDPR and ISO 13485:2016 compliance and cites a Class IIb CE mark under EU MDR. Eyenuk cites CPT 92229 for US billing.

Who sells it and for how much?

The founder is Kaushal Solanki, Ph.D. The About page gives no founding year, funding or headcount. Eyenuk claims 500K+ patients scanned and names Temple Health, Nebraska Medicine, University of Kentucky HealthCare and Diabetes-Klinik in Germany as customers.

Eyenuk publishes no per patient or subscription price. Its FAQ says CPT 92229 "has an established national Medicare price".

The verdict

Grading diabetic retinopathy from a fundus camera without a specialist read is the job EyeArt is sold for, in clinic networks and screening programs. AMD and glaucoma calls are described by the vendor as expanding, so they carry no FDA clearance in the K numbers found. The FAQ cites CPT 92229 for billing and gives no dollar figure.

Editorial review last generated November 2, 2026. Synthesized from clinician sentiment, peer-reviewed coverage, and our editorial silo picks. Refined by hand where vendor facts change.

What it costs

All tiers are custom-quoted. The vendor does not publish per-provider list pricing.

TierMonthlyAnnualNotes
EnterpriseNot statedNot statedNo public price.

Source: vendor pricing page. Last checked November 2, 2026.

What deploys cleanly

Carries FDA 510(k), CE, HIPAA, SOC 2 Type II, ISO 13485:2016 per vendor documentation. Independent attestation review is the buyer's responsibility before clinical deployment.

What the literature says

5 peer-reviewed studies indexed on PubMed evaluate Eyenuk in clinical contexts. The most relevant are shown below, ranked by editorial relevance score combining title match, study design, recency, and journal tier.

Diagnostic Accuracy of the EyeArt Artificial Intelligence System for Diabetic Retinopathy: A Systematic Review and Meta-Analysis. (opens in new tab)
Guedes Guedes II, Santana PS, Ramos de Miguel Á, et al.· Ophthalmologica· 2026Meta-Analysis
Diabetic retinopathy (DR) persists as a predominant cause of preventable vision loss globally, with its prevalence escalating in conjunction with the diabetes epidemic. Efficient, automated screening is needed to enable earlier detection of DR at scale. Artificial intelligence-driven platforms, such as EyeArt® (Eyenuk Inc.), offer a scalable solution with potential to alleviate the burden on healthcare systems. A systematic review and meta-analysis were conducted following PRISMA and MOOSE guidelines. This review was prospectively registered in PROSPERO (CRD42024571137). Observational st…
A pilot cost-analysis study comparing AI-based EyeArt® and ophthalmologist assessment of diabetic retinopathy in minority women in Oslo, Norway. (opens in new tab)
Karabeg M, Petrovski G, Hertzberg SN, et al.· Int J Retina Vitreous· 2024
Diabetic retinopathy (DR) is the leading cause of adult blindness in the working age population worldwide, which can be prevented by early detection. Regular eye examinations are recommended and crucial for detecting sight-threatening DR. Use of artificial intelligence (AI) to lessen the burden on the healthcare system is needed. To perform a pilot cost-analysis study for detecting DR in a cohort of minority women with DM in Oslo, Norway, that have the highest prevalence of diabetes mellitus (DM) in the country, using both manual (ophthalmologist) and autonomous (AI) grading. This is the firs…
[Use of Artificial Intelligence in Diabetic Retinopathy Screening: Experience in a Health Service in Santiago, Chile]. (opens in new tab)
Ibañez-Bruron MC, Cruzat A, Ordenes-Caviere G, et al.· Rev Med Chil· 2024
Early detection of diabetic retinopathy is critical for preventing vision loss. To evaluate the use of artificial intelligence (AI) for screening sight threatening diabetic retinopathy (DR) in a public Hospital in Chile. The mydriatic retinal photographs of 366 participants were uploaded for analysis by EyeArt, a cloud-based AI software developed by Eyenuk (Los Ángeles, USA). Diagnostic accuracy was calculated by comparing its results with the clinical evaluation of the fundus by an ophthalmologist. Participants with severe non-proliferative DR or worse were considered as positive cases.…
Comparison of Validity and Reliability of Manual Consensus Grading vs. Automated AI Grading for Diabetic Retinopathy Screening in Oslo, Norway: A Cross-Sectional Pilot Study. (opens in new tab)
Karabeg M, Petrovski G, Holen K, et al.· J Clin Med· 2025
Diabetic retinopathy (DR) is a leading cause of visual impairment worldwide. Manual grading of fundus images is the gold standard in DR screening, although it is time-consuming. Artificial intelligence (AI)-based algorithms offer a faster alternative, though concerns remain about their diagnostic reliability.A cross-sectional pilot study among patients (≥18 years) with diabetes was established for DR and diabetic macular edema (DME) screening at the Oslo University Hospital (OUH), Department of Ophthalmology, and the Norwegian Association of the Blind and Partially Sighted (NABP). The…
Comparative assessment of diagnostic agreement between artificial intelligence and general practitioners in diabetic retinopathy screening using non-mydriatic fundus photography. (opens in new tab)
Alcalá-Millán M, Romero-Vigara JC, Trillo-Calvo E· Prim Care Diabetes· 2026Observational
To assess the diagnostic agreement between an artificial intelligence (AI) system and general practitioners (GPs) interpreting fundus photographs for diabetic retinopathy (DR) screening, using the ophthalmologists' assessment as the reference standard. We performed a cross-sectional study of 500 primary care patients with type 2 diabetes (T2DM). Each underwent two 45° non-mydriatic fundus photos per eye. Images were independently evaluated by three trained GPs, a deep learning-based AI system (EyeArt v3.0.0 (Eyenuk Inc.)), and an ophthalmologist (reference standard). Diagnostic agreement…

See all on PubMed (opens in new tab)

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