Abstract
Advanced chronic liver disease (CLD) affects 2–5% of the general population, and accessible screening tools are needed in primary care. Here we conducted a pragmatic trial to assess whether an electrocardiogram (ECG)-based machine learning (ECG-ML) model enables early detection of advanced CLD. In this trial, 98 primary care teams were cluster randomized to intervention (access to ECG-ML results; 123 clinicians) or usual care (122 clinicians). Clinicians in the intervention arm were notified of a positive ECG-ML result, indicating higher risk of advanced CLD. The primary endpoint was new diagnosis of CLD with advanced fibrosis within 180 days of ECG, confirmed by sequential liver disease assessments. A total of 15,596 adults underwent 12-lead ECGs as part of routine care and met inclusion criteria (N = 8,034 intervention and N = 7,562 control). The intervention significantly increased new diagnoses of advanced CLD in the overall cohort (1.0% versus 0.5% in the control arm; odds ratio (OR) 2.09, 95% confidence interval (CI) 1.22–3.55, P = 0.007). Among ECG-ML-positive patients, advanced CLD was more frequent in the intervention arm (4.4% versus 1.1%; OR 4.37, 95% CI 1.94–9.88, P < 0.001). The intervention also increased the detection of any fibrosis (secondary endpoint) in the overall cohort (1.7% versus 0.5%; OR 3.17, 95% CI 1.86–5.40, P < 0.001) and among ECG-ML-positive patients (8.4% versus 1.1%; OR 8.03, 95% CI 3.50–18.4, P < 0.001). The diagnostic yield below epidemiological estimates probably reflects variable clinician adherence to artificial intelligence-driven recommendations. These results demonstrate that an ECG-based machine learning model, followed by targeted testing based on risk factors, may aid case finding of advanced CLD in routine primary care. ClinicalTrials.gov registration: NCT05782283.
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Data availability
The dataset is not publicly available as it comprises electronic health records consented for research use exclusively by Mayo Clinic investigators. Publicly sharing the data without additional consent or ethical approval could compromise patient privacy and violate the original ethical guidelines. Investigators interested in conducting additional analyses for noncommercial use may submit a request to the corresponding author (D.A.S.) and will be conducted in collaboration with Mayo Clinic. Requests will typically be processed within 90 days.
Code availability
The ECG-AI algorithm, previously published, cannot be made publicly available owing to proprietary intellectual property protections (patent US20230218238A1). Furthermore, it cannot be implemented in routine clinical practice until Food and Drug Administration approval is obtained. However, the algorithm is available upon request for use in research studies and can be provided by the corresponding author (D.A.S.).
Change history
10 February 2026
A Correction to this paper has been published: https://doi.org/10.1038/s41591-026-04261-5
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Acknowledgements
Funding from the Mayo Clinic MAX Innovation Award, UL1TR002377. D.A.S. is funded by National Institutes of Health AA26974 and DK130181. D.A.S. has consulted for Mallinckrodt, Evive, Resolution Therapeutics, BioVie, AstraZeneca, Iota and PharmaIN. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
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Conceptualization by D.A.S., Z.I.A., P.A.N. and D.R. Methodology by D.A.S., B.A.K., R.J.L., K.L., P.R., A.O., J.C.A. and Z.I.A. Data curation by D.A.S., A.C., B.A.K., R.J.L., P.R. and J.C.A. Writing of the original draft by D.A.S., A.C., B.A.K. and R.J.L. Writing, reviewing and editing by all authors. All authors read and approved the final manuscript.
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The ECG-enabled machine learning model to detect advanced CLD was licensed by Mayo Clinic to Anumana. D.A.S., P.R., J.C.A., P.A.F., P.S.K., Z.I.A., P.A.N. and V.H.S. may benefit financially from its commercialization (patent US20230218238A1). Z.I.A. is a member of the scientific advisory board for Anumana, an AI company, and is a consultant for Anumana, AliveCor and XAI.health. P.A.F. is a member of the scientific advisory board for Anumana, an AI company. The other authors declare no competing interests.
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Simonetto, D.A., Rushlow, D., Liu, K. et al. Detection of undiagnosed liver cirrhosis via AI-enabled electrocardiogram: a pragmatic, cluster-randomized clinical trial. Nat Med 32, 160–167 (2026). https://doi.org/10.1038/s41591-025-04058-y
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DOI: https://doi.org/10.1038/s41591-025-04058-y
