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Yes, retinal features may be associated with cardiovascular risk, and emerging AI tools can use retinal photographs to flag people who may need a standard risk assessment. But an eye exam or retinal scan cannot diagnose heart disease, show that coronary arteries are blocked, or replace evaluation by a clinician. The distinction matters: retinal screening research has so far tested risk estimates, not whether a scan can confirm heart disease in an individual.
What can a retinal scan show about heart risk?
A retinal photograph captures structures at the back of the eye, including small blood vessels. Because the retina can be imaged non-invasively, retinal imaging is already used in routine eye care. Research has found associations between retinal-layer thickness or vessel features and systemic health, including cardiovascular risk. The National Eye Institute’s 2024 summary, for example, describes research linking retinal changes with future risk across several systemic disease categories, including cardiac disease (NEI, January 24, 2024).
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An association is not a diagnosis. A retinal photo does not directly measure cholesterol or blood pressure, reveal plaque in coronary arteries, or show whether someone is having a heart attack. It may provide information that a risk model can use to identify people who warrant follow-up.
What did the 2026 CLAiR study find?
A prospective evaluation reported by the American College of Cardiology (ACC) on March 30, 2026, tested CLAiR, an AI system that analyzes retinal photographs. It enrolled 874 adults ages 40–75 at 10 U.S. eye-care and primary-care sites. Participants were not taking lipid-lowering medication and did not have known atherosclerosis. In this study sample, 26% had a standard-estimator 10-year atherosclerotic cardiovascular disease (ASCVD) risk of at least 7.5% (ACC, March 30, 2026).
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The comparison was with a risk estimate, not a heart-disease diagnosis
The researchers compared CLAiR’s classification with a standard 10-year risk estimate calculated using clinical information: age, sex, smoking status, blood pressure, and cholesterol. CLAiR’s reported 91.1% sensitivity and 86.2% specificity describe how it classified people against that risk-estimator threshold in this study. They are not accuracy figures for detecting confirmed coronary artery disease or other existing heart conditions.
| Study detail | What was reported |
|---|---|
| Participants and setting | 874 adults ages 40–75 at 10 U.S. eye-care and primary-care sites; participants were not taking lipid-lowering medication and had no known atherosclerosis (ACC, March 30, 2026). |
| Reference classification | Standard-estimator 10-year ASCVD risk of at least 7.5%, calculated using age, sex, smoking status, blood pressure, and cholesterol (ACC, March 30, 2026). |
| Share above the threshold | 26% of this study sample—not a population-wide prevalence estimate (ACC, March 30, 2026). |
| Sensitivity and specificity | 91.1% and 86.2%, respectively, for agreement with the standard risk-estimator classification, not confirmed disease (ACC, March 30, 2026). |
| Usable photographs | 94% of images acquired in this study were usable by CLAiR; this does not establish the same rate for every camera, clinic, or patient (ACC, March 30, 2026). |
| Workflow reported | Imaging took about five minutes and an algorithm result took about 30 seconds in the reported study workflow; timing may differ elsewhere (ACC, March 30, 2026). |
The study is evidence that retinal-image analysis may help flag people whose estimated cardiovascular risk merits attention. It does not show that the tool prevents heart attacks, improves health outcomes, or detects existing blocked arteries. The ACC says further work is needed to connect people with elevated results to primary-care evaluation and preventive treatment.
How is AI risk screening different from a routine eye exam?
| Approach | Purpose and output | What it does not establish |
|---|---|---|
| Routine retinal imaging or eye examination | Assesses ocular health; imaging provides a view of retinal structures and vessels. | It does not, by itself, diagnose cardiovascular disease. |
| AI analysis of a retinal photograph for cardiovascular risk | An emerging screening approach that analyzes image patterns to estimate whether someone may meet a risk category. | A risk flag is not a diagnosis, a direct heart scan, or a treatment decision. |
These are not interchangeable tests. A routine eye exam is for eye health; an AI cardiovascular-risk result would be a prompt for clinical follow-up. The ACC reported that the CLAiR evaluation used standard retinal cameras, but the approach still requires validation and a workable route into care. Retinal imaging may also involve an additional fee and is not covered by all U.S. vision plans as part of a standard visit.
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- Ask what the result represents. Find out whether it is an eye-health finding or an AI-generated cardiovascular risk flag, and which risk category or threshold it refers to.
- Arrange a standard assessment with a clinician. A primary-care clinician can review established risk factors, including blood pressure, cholesterol, smoking, age, and medical history. Do not use the retinal result alone to decide whether to start, stop, or change medication.
- Follow the clinician’s prevention plan. Any treatment or further testing should be based on clinical assessment, not on a retinal photograph in isolation.
Michael V. McConnell, MD, the study’s lead author and a Stanford University clinical professor of medicine, said the approach would not replace standard cardiovascular risk evaluation. He also said that people with elevated results need clear pathways to see a clinician and receive guideline-based preventive care. ACC notes that McConnell serves as chief health officer at Toku, the company that created CLAiR.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How well do these findings apply to different people?
The CLAiR study included 19% Black or African American participants and 26% Hispanic participants, but those figures alone do not establish equal performance across all groups. The results also may not apply to younger people, people taking lipid-lowering medication, or people with known atherosclerosis, who were outside the described study population. CLAiR was not designed for pregnant people or those with advanced eye disease.
Other retinal-risk research offers context but not a clinical guarantee. In a separate UK Biobank analysis of 1,101 people with prediabetes or type 2 diabetes followed for a median of 11 years, cardiovascular events occurred in 8.2% of the low-risk group, 15.2% of the moderate-risk group, and 18.5% of the high-risk group, according to an American Heart Association report from 2023. These are findings in that cohort, which was predominantly white, not universal probabilities for an individual (AHA, 2023).
What remains uncertain?
The 2026 CLAiR evaluation is newer prospective evidence than earlier statements that retinal AI was not ready for clinical use. It still compared an image-based classification with a standard risk estimate rather than confirmed cardiovascular outcomes. Earlier American Academy of Ophthalmology educational material described this type of prediction as promising but not then ready for clinical application, citing the need for prospective studies and real-world evidence (AAO, 2022).
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesAn NHLBI-workshop roadmap identifies continuing needs for standardized, high-quality image capture; diverse longitudinal cohorts; improved retinal biomarker measurement; and integration with clinical data and privacy protections (NHLBI workshop roadmap, 2024). The ACC report says the CLAiR evaluation will support an FDA submission; the cited coverage does not establish FDA clearance or authorization, or that the system is broadly available in clinical practice. A large genetic study identifying 259 loci associated with retinal thickness is a finding about genetics and the retina, not a heart-screening performance result (NEI, 2024).
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