AI can find patterns in an electrocardiogram (ECG) that are associated with reduced heart-pumping function or a higher chance of future heart-failure hospitalization. Studies using cohorts in the United States, United Kingdom and Brazil support AI-ECG as a promising screening or risk-stratification aid—not as a standalone diagnosis, a replacement for echocardiography, or a proven way to prevent heart failure. Evidence across countries is not proof of equal performance or readiness for global use.
What an AI ECG can—and cannot—tell you
An ECG records the heart’s electrical activity. AI models can analyze patterns in that recording that may be difficult to identify by eye. In the studies discussed here, the models were trained to detect signals associated with left ventricular systolic dysfunction (LVSD)—weakened pumping by the heart’s main chamber—or to estimate future risk of heart-failure (HF) hospitalization.
Those are related but different questions. Reduced left ventricular ejection fraction (LVEF) is one form of heart dysfunction; HF includes multiple conditions, causes and ejection-fraction categories. A model that flags possible reduced LVEF does not thereby diagnose every form of HF. A positive output is a reason for clinical assessment, not confirmation that someone has HF.
What the multinational studies found
Two separate 2025 studies tested different AI approaches in cohorts from three countries. Their results are evidence of predictive association in those cohorts—not absolute risk estimates for an individual, proof that AI caused better outcomes, or evidence that screening improves survival.
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| Study and ECG input | Participants and follow-up | Association with future HF | Discrimination |
|---|---|---|---|
| European Heart Journal, 2025: AI applied to 12-lead ECG images to detect LV systolic dysfunction | Yale New Haven Health System (YNHHS): 231,285 participants without baseline HF; 4,472 primary HF hospitalizations over a median 4.5 years. UK Biobank: 42,141 participants. ELSA-Brasil: 13,454 participants. | A positive screen was associated with higher risk of new-onset HF hospitalization. Age- and sex-adjusted hazard ratios: YNHHS 3.88 (95% CI 3.63–4.14); UK Biobank 12.85 (6.87–24.02); ELSA-Brasil 23.50 (11.09–49.81). | 0.718 in YNHHS, 0.769 in UK Biobank and 0.810 in ELSA-Brasil. |
| JAMA Cardiology, 2025: noise-adapted model using lead-I single-lead ECG data | YNHHS: 192,667 patients; UK Biobank: 42,141 participants; ELSA-Brasil: 13,454 participants. | A positive screen was associated with three- to seven-fold higher HF risk across the cohorts. | C-statistics: YNHHS 0.723 (95% CI 0.694–0.752); UK Biobank 0.736 (0.606–0.867); ELSA-Brasil 0.828 (0.692–0.964). |
Discrimination describes how well a model separates people who experience an outcome from those who do not; it is not the chance that a particular person will develop HF. Hazard ratios compare event rates between groups over time and are not absolute probabilities. The studies’ values should not be directly ranked against one another: their ECG inputs, models and analyses differ.
In the JAMA Cardiology study, adding the AI-ECG probability improved comparison metrics over PCP-HF and PREVENT risk scores in the study cohorts. That finding does not establish that using the model in routine care improves patient outcomes.
How well does AI ECG detect low ejection fraction?
A 2022 systematic review and meta-analysis combined 11 studies with 104,737 participants. It reported a pooled area under the curve (AUC) of 0.986, sensitivity of 0.95 (95% CI 0.86–0.98) and specificity of 0.98 (0.95–0.99). These pooled results describe earlier studies with different algorithms, populations and reference standards; they should not be treated as the expected accuracy of a current product in everyday practice. The review found high risk of bias in patient selection in eight studies and noted variation among study methods.
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A 2026 Singapore Agency for Care Effectiveness evidence brief reviewed Eko ELEFT and Anumana ECG-AI LEF for detecting LVEF of 40% or less. Across studies included in that brief, reported sensitivity ranged from 76.5% to 100%, specificity from 78.3% to 97%, negative predictive value (NPV) from 98% to 100%, and positive predictive value (PPV) from 16.4% to 43.3%. The brief noted that PPV varied with the low prevalence of reduced LVEF in study populations. These are ranges across included studies, not a head-to-head ranking or a guarantee for any particular patient or setting.
Does a positive result mean you have heart failure?
No. A positive AI result means the model detected a pattern associated with its target. It does not establish the diagnosis. Clinicians interpret it alongside symptoms, medical history, examination and other tests; when reduced LVEF is suspected, echocardiography can assess heart structure and pumping function. The Singapore evidence brief evaluates AI-ECG against echocardiography for LVEF of 40% or less, reflecting its role as a screening input rather than a substitute for that assessment.
Predictive value depends partly on how common the target condition is in the population being tested. In a group where reduced LVEF is uncommon, a positive result can be false even when a test has strong sensitivity and specificity. The published PPV range in the Singapore brief illustrates why a model output needs context and follow-up.
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Can AI detect heart failure before symptoms—or from a smartwatch?
The 2025 cohort studies found associations between positive AI-ECG screens and later HF hospitalization among people without baseline HF. That supports the possibility of identifying risk before a recorded hospitalization; it does not show that the studies screened an asymptomatic population in a way that prevents disease or improves survival. It also does not mean every person with an abnormal result will develop HF.
The lead-I study used data isolated from conventionally obtained ECGs and adapted the model to tolerate noise to simulate wearable conditions. It was not a prospective trial of people screening themselves with smartwatches or portable monitors. The authors said prospective research using wearable and portable ECG devices is needed. A device that records an ECG is not, by that fact alone, an AI heart-failure detector.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsWhy evidence from three countries is not yet global readiness
Validation in cohorts from the US, UK and Brazil broadens the evidence beyond one setting, but it does not establish equal accuracy, access, regulatory authorization or clinical benefit worldwide. Results can depend on the ECG format and signal quality, the population used to train and validate a model, local disease prevalence, and whether care systems can provide timely follow-up.
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- Input compatibility: The image-based 12-lead approach and the noise-adapted single-lead approach are separate models, not interchangeable tests.
- Population calibration: A model’s output needs suitable validation and interpretation in the population where it will be used; discrimination alone does not provide a calibrated personal probability.
- Clinical pathway: Screening is useful only if positive results can be assessed and confirmed appropriately.
- Local readiness: Regulatory status, devices, workflow and access to follow-up must be established for the relevant country and intended use.
Where ECG AI fits in heart-failure care
The 2022 AHA/ACC/HFSA heart-failure guideline states: “For all patients presenting with HF, a 12-lead ECG should be performed at the initial encounter to optimize management.” This recommendation concerns people presenting with HF; it is not an endorsement of AI screening in people without symptoms. The guideline also describes laboratory evaluation and additional diagnostic studies as part of assessment.
For now, the evidence supports viewing AI-ECG as a potential triage or risk-stratification aid within clinical care. It may help flag people who warrant further evaluation or add information to existing risk assessment. It does not establish a universal screening program, a standalone diagnosis of HF, or a replacement for clinical evaluation and confirmatory testing.
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