Precision medicine and data
Eko Health SENSORA
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Last updated: June 14, 2026
Back to directoryAnumana ECG-AI medical AI product profile
Cardiovascular AI platform using standard 12-lead ECGs to surface signals such as low ejection fraction, pulmonary hypertension, and cardiac amyloidosis for clinician follow-up.
Precision medicine and data
Best fit
Health systems evaluating FDA-cleared cardiac detection support that can fit existing ECG, EHR, and cardiology referral workflows.
- Primary use case
- FDA-cleared ECG-AI algorithms for identifying cardiovascular risk signals from standard 12-lead ECG workflows
- Audience
- Cardiology, primary care, emergency medicine, health-system innovation, ECG management, and clinical informatics teams
- Risk level
- High
- Pricing signal
- Enterprise and health-system pricing; request current algorithm, integration, reimbursement, and regional availability terms.
- Official sources
- 6 official sources
Compare within workflow: Precision medicine and data · comparison shortlist · source index
Regulatory snapshot
| Status | FDA-cleared cardiovascular machine-learning notification software for specific ECG-AI algorithms; each algorithm has its own intended-use boundary. |
|---|---|
| Review route | 510(k), including K232699 for the Low Ejection Fraction AI-ECG Algorithm and K252360 for the ECG-AI Pulmonary Hypertension 12-Lead algorithm. |
| Intended use | Uses standard resting 12-lead ECG data to aid earlier detection or screening for defined cardiac conditions, including LVEF less than or equal to 40% in adults at risk for heart failure and elevated mean pulmonary arterial pressure in adults presenting with dyspnea. |
| Verification note | Confirm the deployed algorithm, compatible ECG source, eligible adult population, paced-rhythm and monitoring exclusions, EMR or ECG-management integration, clinician review, referral pathway, and whether a negative result should still trigger evaluation for high-risk patients. |
| Source | www.accessdata.fda.gov / cdrh_docs / pdf25 |
Regulatory, privacy, evidence, and workflow lens
Product-specific review. These product-specific signals summarize what the cited sources imply before treating Anumana ECG-AI as safe for a local clinical, operational, or research workflow.
| Regulatory / FDA | Match each deployment to the exact cleared algorithm and intended use, including K232699 for low ejection fraction and K252360 for pulmonary hypertension; do not generalize clearance across future or investigational cardiac conditions. |
|---|---|
| Privacy | Review ECG, EHR, result-routing, audit-log, customer-support, and integration data flows; Anumana says the pulmonary hypertension algorithm runs within the health-system environment, but contract and architecture review still matter. |
| Evidence | Evaluate local performance by ECG source, patient mix, prevalence, care setting, downstream echo or referral pathway, false-positive burden, and whether published sensitivity and specificity match the intended workflow. |
| Workflow | Best governed as clinician-reviewed cardiac detection support with defined ECG-system integration, result display, referral criteria, cardiology escalation, monitoring, and patient communication rules. |
Where Anumana ECG-AI fits
Anumana describes ECG-AI as a cardiology AI platform that applies FDA-cleared algorithms to standard 12-lead ECGs; FDA records list cleared Anumana ECG-AI algorithms for low ejection fraction and pulmonary hypertension, and Anumana's materials describe health-system workflow integration and U.S. commercial availability for ECG-AI.
Not for: Standalone diagnosis, therapy selection, use outside cleared indications, or deployment without cardiology, primary-care, ECG-system, and informatics governance.
What to verify before using Anumana ECG-AI
- Which ECG-AI algorithm is in scope: low ejection fraction, pulmonary hypertension, cardiac amyloidosis, or another pipeline module.
- FDA 510(k) record, product code, intended population, ECG acquisition requirements, and whether the algorithm is commercially available in your country.
- How ECG data, EHR data, alerts, audit logs, and support access are handled when the system runs inside or connects to the health-system environment.
- Clinical workflow impact on ECG ordering, echo utilization, cardiology referral, false positives, missed cases, alert fatigue, and post-deployment monitoring.
Source links
Use these links to confirm current claims, terms, regulatory status, pricing, and deployment requirements.
- anumana.ai / our-platform
- anumana.ai / for-healthcare-providers
- anumana.ai / anumana-secures-fda-clearance-for-first-of-its-kind-ecg-ai-algorithm-for-early-detection-of-pulmonary-hypertension
- www.accessdata.fda.gov / scripts / cdrh
- www.accessdata.fda.gov / scripts / cdrh
- anumana.ai / privacy-policy
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