Artificial intelligence–enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial
Abstract
We have conducted a pragmatic clinical trial aimed to assess whether an electrocardiogram (ECG)-based, artificial intelligence (AI)-powered clinical decision support tool enables early diagnosis of low ejection fraction (EF), a condition that is underdiagnosed but treatable. In this trial ( NCT04000087 ), 120 primary care teams from 45 clinics or hospitals were cluster-randomized to either the intervention arm (access to AI results; 181 clinicians) or the control arm (usual care; 177 clinicians). ECGs were obtained as part of routine care from a total of 22,641 adults (N = 11,573 intervention; N = 11,068 control) without prior heart failure. The primary outcome was a new diagnosis of low EF (≤50%) within 90 days of the ECG. The trial met the prespecified primary endpoint, demonstrating that the intervention increased the diagnosis of low EF in the overall cohort (1.6% in the control arm versus 2.1% in the intervention arm, odds ratio (OR) 1.32 (1.01-1.61), P = 0.007) and among those who were identified as having a high likelihood of low EF (that is, positive AI-ECG, 6% of the overall cohort) (14.5% in the control arm versus 19.5% in the intervention arm, OR 1.43 (1.08-1.91), P = 0.01). In the overall cohort, echocardiogram utilization was similar between the two arms (18.2% control versus 19.2% intervention, P = 0.17); for patients with positive AI-ECGs, more echocardiograms were obtained in the intervention compared to the control arm (38.1% control versus 49.6% intervention…
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The EAGLE trial increased new diagnoses of low EF from 1.6% in the control arm to 2.1% in the intervention arm (OR 1.32, 95% CI 1.01-1.61; P = 0.007), equivalent to a 32% relative increase over usual care.
“The trial met this endpoint, increasing new diagnoses from 1.6% in the control arm to 2.1% in the intervention arm (OR 1.32, 95% CI 1.01–1.61; P = 0.007), equivalent to a 32% relative increase over usual care.14”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [14]
In EAGLE, only 49.6% of intervention-arm patients with a positive AI-ECG result underwent echocardiography.
“In EAGLE, only 49.6% of intervention-arm patients with a positive AI-ECG result underwent echocardiography, indicating that follow-up imaging still did not occur in roughly half of AI-positive outpatients even when the result was visible to clinicians.14”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [14]
Unlike low-LVEF AI-ECG, broad SHD screening is still supported mainly by retrospective multicenter validation and nonrandomized prospective or population-based cohorts, and randomized implementation data, hard downstream outcome trials, and validated gatekeeper protocols for safely deferring echocardiography remain unavailable.
“Even so, unlike low-LVEF AI-ECG—which already has pragmatic randomized trial evidence—broad SHD screening is still supported mainly by retrospective multicenter validation and nonrandomized prospective or population-based cohorts, and randomized implementation data, hard downstream outcome trials, and validated gatekeeper protocols for safely deferring echocardiography remain unavailable.14,34”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [14]
AI-enabled detection improved case finding without materially increasing overall echocardiography utilization.
“Importantly, AI-enabled detection improved case finding without materially increasing overall echocardiography utilization, suggesting referral redirection rather than indiscriminate imaging expansion.”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [14]
The main contribution of EAGLE was to elevate the evidentiary level from model-performance literature to pragmatic care-pathway evidence.
“The main contribution of EAGLE was not to re-establish that AI-ECG can detect low LVEF, which had already been shown in earlier derivation and validation studies, but to elevate the evidentiary level from model-performance literature to pragmatic care-pathway evidence.14”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [14]
Follow-up imaging still occurred in only about half of AI-positive outpatients.
“Even so, follow-up imaging still occurred in only about half of AI-positive outpatients, and later inpatient pragmatic data likewise underscored a persistent gap between algorithmic flagging and downstream clinical action.14,37”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [14]
Low-LVEF detection is the most mature use case, having already progressed from model development to randomized trials and early implementation studies.
“Low-LVEF detection is the most mature use case, having already progressed from model development to randomized trials and early implementation studies.”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [14]
The regulatory maturity of AI-ECG for SHD screening remains uneven.
“The regulatory maturity of AI-ECG for SHD screening remains uneven.”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [14]
Published AI-ECG studies have so far focused predominantly on opportunistic, clinic-based, or referral-enrichment strategies rather than organized population screening programmes with predefined invitation, recall, quality assurance, and long-term outcome monitoring structures.
“Published AI-ECG studies have so far focused predominantly on opportunistic, clinic-based, or referral-enrichment strategies rather than organized population screening programmes with predefined invitation, recall, quality assurance, and long-term outcome monitoring structures.3,14”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [14]
The EAGLE study showed an increase in detecting low EF from 178 (1.6%) in the control group to 244 (2.1%) in the intervention group (OR 1.32; 95% CI 1.01-1.61; P=.007).
“The study showed an increase in detecting low EF from 178 (1.6%) in the control group to 244 (2.1%) for the intervention group (odds ratio, 1.32; 95% CI, 1.01 to 1.61]; P¼0.007).”
From Clinician Adoption of an Artificial Intelligence Algorithm to Detect Left Ventricular Systolic Dysfunction in Primary Care. (2022) · cites this paper as [13]
In the EAGLE study, 358 primary care clinicians were randomized to intervention or control across 48 upper Midwest clinics/hospitals; the primary endpoint was a new diagnosis of EF less than or equal to 50% within 3 months.
“In this study, 358 primary care clinicians were randomized to either an intervention group (access to AI screening results) or a control group (usual care) across 48 upper Midwest clinics/hospitals.The primary endpoint was a new diagnosis of EF less than or equal to 50% within 3 months.”
From Clinician Adoption of an Artificial Intelligence Algorithm to Detect Left Ventricular Systolic Dysfunction in Primary Care. (2022) · cites this paper as [13]
The trial was registered (ClinicalTrials.gov number NCT04000087).
“The trial was registered (ClinicalTrials.govnumber NCT04000087), and the study protocol and materials have been previously published.13”
From Clinician Adoption of an Artificial Intelligence Algorithm to Detect Left Ventricular Systolic Dysfunction in Primary Care. (2022) · cites this paper as [13]
The EAGLE trial was a pragmatic cluster-randomized trial evaluating AI-guided screening for low ejection fraction in primary care.
“A major step toward clinical implementation came with the EAGLE trial, a pragmatic cluster-randomized trial evaluating AI-guided screening for low ejection fraction in primary care.”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [19]
In the EAGLE trial, the intervention increased new diagnoses of low ejection fraction within 90 days, particularly among patients with a positive AI screen.
“The intervention increased new diagnoses of low ejection fraction within 90 days, particularly among patients with a positive AI screen, and increased targeted echocardiography in the AI-positive subgroup without substantially increasing echocardiography use across the entire population [19].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [19]
In the EAGLE trial, the intervention increased targeted echocardiography in the AI-positive subgroup without substantially increasing echocardiography use across the entire population.
“The intervention increased new diagnoses of low ejection fraction within 90 days, particularly among patients with a positive AI screen, and increased targeted echocardiography in the AI-positive subgroup without substantially increasing echocardiography use across the entire population [19].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [19]
The EAGLE trial demonstrated that model output could alter real-world diagnostic behavior.
“Unlike most retrospective AI-ECG studies, the EAGLE trial demonstrated that model output could alter real-world diagnostic behavior.”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [19]
The EAGLE trial showed that results delivered through the EHR can increase detection and targeted echocardiography, but it did not establish the optimal alert format, recipient, timing, threshold, or follow-up pathway.
“The EAGLE trial showed that results delivered through the EHR can increase detection and targeted echocardiography, but it did not establish the optimal alert format, recipient, timing, threshold, or follow-up pathway.”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [19]
Evidence from reduced-ejection-fraction models and the EAGLE trial suggests that AI-ECG screening can identify subclinical disease and influence diagnostic evaluation.
“Evidence from reduced-ejection-fraction models and the EAGLE trial suggests that this approach can identify subclinical disease and influence diagnostic evaluation [4,19].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [19]
The EAGLE trial delivered AI results through the electronic health record while clinicians retained responsibility for ordering confirmatory echocardiography.
“The EAGLE trial, discussed above, provides an early example of this model: AI results were delivered through the electronic health record (EHR) while clinicians retained responsibility for ordering confirmatory echocardiography [19].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [19]
Cites
2013 ACCF/AHA Guideline for the Management of Heart Failure
Yancy, C. W., Jessup, M., Bozkurt, B., Butler, J., Casey, D. E., Drazner, M. H., Fonarow, G. C., Geraci, S. A., Horwich, T., Januzzi, J. L., et al. (2013) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2013.05.019 · Cited by 12,536 · see citing works
(1992) · New England Journal of Medicine · doi:10.1056/nejm199209033271003 · Cited by 3,725 · see citing works
Burden of Systolic and Diastolic Ventricular Dysfunction in the Community
Redfield, M. M., Jacobsen, S. J., Burnett, J. C. J., Mahoney, D. W., Bailey, K. R., & Rodeheffer, R. J. (2003) · JAMA · doi:10.1001/jama.289.2.194 · Cited by 2,989 · see citing works
Liu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., Denniston, A. K., The SPIRIT-AI and CONSORT-AI Working Group, SPIRIT-AI and CONSORT-AI Steering Group, Chan, A.-W., Darzi, A., Holmes, C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1034-x · Cited by 1,421 · see citing works
Attia, Z. I., Kapa, S., Lopez-Jimenez, F., McKie, P. M., Ladewig, D. J., Satam, G., Pellikka, P. A., Enriquez-Sarano, M., Noseworthy, P. A., Munger, T. M., et al. (2019) · Nature Medicine · doi:10.1038/s41591-018-0240-2 · Cited by 1,389 · see citing works
Self-citationLiu, X., Rivera, S. C., Moher, D., Calvert, M. J., & Denniston, A. K. (2020) · BMJ · doi:10.1136/bmj.m3164 · Cited by 702 · see citing works
Natural history of asymptomatic left ventricular systolic dysfunction in the community
Wang, T. J., Evans, J. C., Benjamin, E. J., Levy, D., LeRoy, E. C., & Vasan, R. S. (2003) · Circulation · doi:10.1161/01.cir.0000085166.44904.79 · Cited by 633 · see citing works
Symptomatic and asymptomatic left-ventricular systolic dysfunction in an urban population
McDonagh, T. A., Morrison, C., Lawrence, A., Ford, I., Tunstall-Pedoe, H., McMurray, J. J. V., & Dargie, H. J. (1997) · The Lancet · doi:10.1016/s0140-6736(97)03033-x · Cited by 631 · see citing works
Jong, P., Yusuf, S., Rousseau, M. F., Ahn, S. A., & Bangdiwala, S. I. (2003) · The Lancet · doi:10.1016/s0140-6736(03)13501-5 · Cited by 377 · see citing works
Davie, A. P., Francis, C. M., Love, M. P., Caruana, L., Starkey, I. R., Shaw, T. R. D., Sutherland, G. R., & McMurray, J. J. V. (1996) · BMJ · doi:10.1136/bmj.312.7025.222 · Cited by 323 · see citing works
Redfield, M. M., Jacobsen, S. J., Burnett, J. C., Mahoney, D. W., Bailey, K. R., & Rodeheffer, R. J. (2003) · ACC Current Journal Review · doi:10.1016/s1062-1458(03)00178-8 · Cited by 281 · see citing works
Plasma Brain Natriuretic Peptide to Detect Preclinical Ventricular Systolic or Diastolic Dysfunction
Redfield, M. M., Rodeheffer, R. J., Jacobsen, S. J., Mahoney, D. W., Bailey, K. R., & Burnett, J. C. J. (2004) · Circulation · doi:10.1161/01.cir.0000130845.38133.8f · Cited by 260 · see citing works
The Epidemiology of Asymptomatic Left Ventricular Systolic Dysfunction: Implications for Screening
Wang, T. J., Levy, D., Benjamin, E. J., & Vasan, R. S. (2003) · Annals of Internal Medicine · doi:10.7326/0003-4819-138-11-200306030-00012 · Cited by 229 · see citing works
Attia, Z. I., Kapa, S., Yao, X., López-Jiménez, F. A., Mohan, T. L., Pellikka, P. A., Carter, R. E., Shah, N. D., Friedman, P. A., & Noseworthy, P. A. (2019) · Journal of Cardiovascular Electrophysiology · doi:10.1111/jce.13889 · Cited by 195 · see citing works
Self-citationMorgan, S., Smith, H. E., Simpson, I. A., Liddiard, G. S., Raphael, H., Pickering, R. M., & Mant, D. (1999) · BMJ · doi:10.1136/bmj.318.7180.368 · Cited by 178 · see citing works
Wen, A., Fu, S., Moon, S., El Wazir, M., Rosenbaum, A., Kaggal, V. C., Liu, S., Sohn, S., Liu, H., & Fan, J. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0208-8 · Cited by 142 · see citing works
Rihal, C. S., Davis, K. B., Kennedy, J. W., & Gersh, B. J. (1995) · The American Journal of Cardiology · doi:10.1016/0002-9149(95)80023-l · Cited by 125 · see citing works
Yao, X., McCoy, R. G., Friedman, P. A., Shah, N. D., Barry, B. A., Behnken, E. M., Inselman, J. W., Attia, Z. I., & Noseworthy, P. A. (2020) · American Heart Journal · doi:10.1016/j.ahj.2019.10.007 · Cited by 98 · see citing works
Self-citationGoetze, J. P., Møgelvang, R., Maage, L., Scharling, H., Schnohr, P., Søgaard, P., Rehfeld, J. F., & Jensen, J. S. (2006) · European Heart Journal · doi:10.1093/eurheartj/ehl406 · Cited by 93 · see citing works
Nielsen, O. W. (2000) · BMJ · doi:10.1136/bmj.320.7229.220 · Cited by 87 · see citing works
Landray, M. (2000) · BMJ · doi:10.1136/bmj.320.7240.985 · Cited by 75 · see citing works
Kaggal, V. C., Elayavilli, R. K., Mehrabi, S., Pankratz, J. J., Sohn, S., Wang, Y., Li, D., Rastegar-Mojarad, M., Murphy, S. P., Ross, J., et al. (2016) · Biomedical Informatics Insights · doi:10.4137/bii.s37977 · Cited by 75 · see citing works
Evidence from Pragmatic Trials during Routine Care — Slouching toward a Learning Health System
Simon, G. E., Platt, R. G., & Hernandez, A. F. (2020) · New England Journal of Medicine · doi:10.1056/nejmp1915448 · Cited by 54 · see citing works
Screening for Asymptomatic Left Ventricular Dysfunction Using B‐Type Natriuretic Peptide
McDonagh, T. A., McDonald, K., & Maisel, A. S. (2008) · Congestive Heart Failure · doi:10.1111/j.1751-7133.2008.tb00002.x · Cited by 35 · see citing works
Hetmanski, D. J., SPARROW, N. J., CURTIS, S., & COWLEY, A. J. (2000) · Heart · doi:10.1136/heart.84.4.440 · Cited by 34 · see citing works
Redfield, M. M., Rodeheffer, R. J., Jacobsen, S. J., et al. (2004) · ACC Current Journal Review · doi:10.1016/j.accreview.2004.08.099 · Cited by 22 · see citing works
Yao, X., McCoy, R. G., Friedman, P. A., Shah, N. D., Barry, B. A., Behnken, E., Inselman, J., Attia, Z. I., & Noseworthy, P. A. (2019) · Data in Brief · doi:10.1016/j.dib.2019.104894 · Cited by 12 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Armoundas, A. A., Narayan, S. M., Arnett, D. K., Spector-Bagdady, K., Bennett, D. A., Celi, L. A., Friedman, P. A., Gollob, M. H., Hall, J. L., Kwitek, A. E., et al. (2024) · Circulation · doi:10.1161/cir.0000000000001201 · Cited by 347 · see citing works
Self-citationArtificial intelligence in surgery
Varghese, C., Harrison, E. M., O’Grady, G., & Topol, E. J. (2024) · Nature Medicine · doi:10.1038/s41591-024-02970-3 · Cited by 287 · see citing works
The Integration of Artificial Intelligence into Clinical Practice
Karalis, V. D. (2024) · Applied Biosciences · doi:10.3390/applbiosci3010002 · Cited by 255 · see citing works
Randomized Clinical Trials of Machine Learning Interventions in Health Care: A Systematic Review
Plana, D., Shung, D. L., Grimshaw, A. A., Saraf, A., Sung, J. J. Y., & Kann, B. H. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.33946 · Cited by 236 · see citing works
Bachtiger, P., Petri, C. F., Scott, F. E., Ri Park, S., Kelshiker, M. A., Sahemey, H. K., Dumea, B., Alquero, R., Padam, P. S., Hatrick, I. R., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00256-9 · Cited by 167 · see citing works
Detection of Left Ventricular Systolic Dysfunction From Electrocardiographic Images
Sangha, V., Nargesi, A. A., Dhingra, L. S., Khunte, A., Mortazavi, B. J., Ribeiro, A. H., Banina, E., Adeola, O., Garg, N., Brandt, C. A., et al. (2023) · Circulation · doi:10.1161/circulationaha.122.062646 · Cited by 150 · see citing works
Randomized Controlled Trials of Artificial Intelligence in Clinical Practice: Systematic Review
Lam, T. Y. T., Cheung, M. F. K., Munro, Y. L., Lim, K. M., Shung, D., & Sung, J. J. Y. (2022) · Journal of Medical Internet Research · doi:10.2196/37188 · Cited by 120 · see citing works
Juhn, Y. J., Ryu, E., Wi, C.-I., King, K. S., Malik, M., Romero-Brufau, S., Weng, C., Sohn, S., Sharp, R. R., & Halamka, J. D. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac052 · Cited by 83 · see citing works
Artificial Intelligence in Cardiovascular Care—Part 2: Applications
Jain, S. S., Elias, P., Poterucha, T., Randazzo, M., Lopez Jimenez, F., Khera, R., Perez, M., Ouyang, D., Pirruccello, J., Salerno, M., et al. (2024) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2024.03.401 · Cited by 74 · see citing works
Gomez-Cabello, C. A., Borna, S., Pressman, S., Haider, S. A., Haider, C. R., & Forte, A. J. (2024) · European Journal of Investigation in Health, Psychology and Education · doi:10.3390/ejihpe14030045 · Cited by 74 · see citing works
Adedinsewo, D. A., Morales-Lara, A. C., Afolabi, B. B., Kushimo, O. A., Mbakwem, A. C., Ibiyemi, K. F., Ogunmodede, J. A., Raji, H. O., Ringim, S. H., Habib, A. A., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03243-9 · Cited by 63 · see citing works
Self-citationPediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling
Mayourian, J., La Cava, W. G., Vaid, A., Nadkarni, G. N., Ghelani, S. J., Mannix, R., Geva, T., Dionne, A., Alexander, M. E., Duong, S. Q., et al. (2024) · Circulation · doi:10.1161/circulationaha.123.067750 · Cited by 59 · see citing works
Martindale, A. P. L., Llewellyn, C. D., de Visser, R. O., Ng, B., Ngai, V., Kale, A. U., di Ruffano, L. F., Golub, R. M., Collins, G. S., Moher, D., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45355-3 · Cited by 58 · see citing works
Bjerkén, L. V., Rønborg, S. N., Jensen, M. T., Ørting, S. N., & Nielsen, O. W. (2022) · Heart Failure Reviews · doi:10.1007/s10741-022-10283-1 · Cited by 50 · see citing works
Rushlow, D. R., Croghan, I. T., Inselman, J. W., Thacher, T. D., Friedman, P. A., Yao, X., Pellikka, P. A., Lopez-Jimenez, F., Bernard, M. E., Barry, B. A., et al. (2022) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2022.04.008 · Cited by 33 · see citing works
Self-citationTricco, A. C., Hezam, A., Parker, A., Nincic, V., Harris, C., Fennelly, O., Thomas, S. M., Ghassemi, M., McGowan, J., Paprica, P. A., et al. (2023) · BMJ Open · doi:10.1136/bmjopen-2022-065845 · Cited by 32 · see citing works
Digital tools in heart failure: addressing unmet needs
Myhre, P. L., Tromp, J., Ouwerkerk, W., Ting, D. S. W., Docherty, K. F., Gibson, C. M., & Lam, C. S. P. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00158-4 · Cited by 31 · see citing works
Mitigating bias in clinical machine learning models
Perez-Downes, J. C., Tseng, A. S., McConn, K. A., Elattar, S. M., Sokumbi, O., Sebro, R. A., Allyse, M. A., Dangott, B. J., Carter, R. E., & Adedinsewo, D. (2024) · Current Treatment Options in Cardiovascular Medicine · doi:10.1007/s11936-023-01032-0 · Cited by 23 · see citing works
Tao, R., Zhang, S., Zhang, R., Shen, C., Ma, J., Cui, J., Chen, Y., Wang, B., Li, H., Xie, X., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-90615-x · Cited by 14 · see citing works
Hsieh, P.-H., Lin, C., Lin, C.-S., Liu, W.-T., Lin, T.-K., Tsai, D.-J., Hung, Y.-J., Chen, Y.-H., Lin, C.-Y., Lin, S.-H., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01735-7 · Cited by 11 · see citing works
Rashid, M., & Khan, M. (2024) · Journal of Communication and Cultural Trends · doi:10.32350/jcct.61.05 · Cited by 8 · see citing works
Naser, J. A., Lee, E., Lopez-Jimenez, F., Noseworthy, P. A., Latif, O. S., Friedman, P. A., Lin, G., Oh, J. K., Scott, C. G., Pislaru, S. V., et al. (2024) · JACC: Advances · doi:10.1016/j.jacadv.2024.101179 · Cited by 8 · see citing works
Self-citationArtificial Intelligence Electrocardiogram and Left Ventricular Systolic Dysfunction in Kenya
Pandey, A., Keshvani, N., Segar, M. W., Kwon, J.-M., Lee, H. S., Bhograj, C., Mashilane, K. I., Jain, N., Mwiti, W., Wambari, E., et al. (2026) · JAMA Cardiology · doi:10.1001/jamacardio.2026.0908 · Cited by 7 · see citing works
Lopez-Jimenez, F., Alger, H. M., Attia, Z. I., Barry, B., Chatterjee, R., Dolor, R., Friedman, P. A., Greene, S. J., Greenwood, J., Gundurao, V., et al. (2025) · American Heart Journal Plus: Cardiology Research and Practice · doi:10.1016/j.ahjo.2025.100528 · Cited by 6 · see citing works
Self-citationAlexandrino, F. B., Schlesinger, R., Bird, J., Lee, E., Deshmukh, A. J., Nkomo, V. T., Oh, J. K., Noseworthy, P. A., Pellikka, P. A., Attia, Z., et al. (2026) · American Heart Journal · doi:10.1016/j.ahj.2025.107337 · Cited by 5 · see citing works
Self-citationTang, Q., Deng, K., Cui, Y., Liu, H., & Qian, B. (2026) · International Journal of General Medicine · doi:10.2147/ijgm.s610298
“14 In a temporally distinct silent-deployment cohort of EchoNext, 45% of patients labeled high risk did not undergo follow-up echocardiography as part of routine clinical care, highlighting a substantial action gap between model-based risk identification and downstream imaging.”
InfluentialDeep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease
Shaulian, S., Zeltser, R., & Makaryus, A. N. (2026) · Diagnostics · doi:10.3390/diagnostics16172834
- Authors
- Yao, X., Rushlow, D. R., Inselman, J. W., McCoy, R. G., Thacher, T. D., Behnken, E. M., Bernard, M. E., Rosas, S. L., Akfaly, A., Misra, A., Molling, P. E., Krien, J. S., Foss, R. M., Barry, B. A., Siontis, K. C., Kapa, S., Pellikka, P. A., Lopez-Jimenez, F., Attia, Z. I., Shah, N. D., Friedman, P. A., & Noseworthy, P. A.
- Affiliations
- Mayo Clinic (US)
- Mayo Clinic Health System (US)
- Corresponding author
- Yao, Xiaoxi
- ORCID
- Yao, Xiaoxi
- Rushlow, David R.
- Inselman, Jonathan W.
- McCoy, Rozalina G.
- Thacher, Thomas D.
- Behnken, Emma M.
- Bernard, Matthew E.
- Rosas, Steven L.
- Akfaly, Abdulla
- Misra, Artika
- Molling, Paul E.
- Foss, Randy M.
- Barry, Barbara A.
- Siontis, Konstantinos C.
- Kapa, Suraj
- Pellikka, Patricia A.
- Attia, Zachi I.
- Shah, Nilay D.
- Friedman, Paul A.
- Noseworthy, Peter A.
- Venue
- Nature Medicine
- Published
- 2021-05
- Volume
- 27
- Issue
- 5
- Pages
- 815-819
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W3159560336
- Cited by
- 424
- References
- 27
- Citations by year
- 2026: 90 · 2025: 93 · 2024: 93 · 2023: 65 · 2022: 68 · 2021: 15
- FWCI
- 13.52
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- artificial intelligence, electrocardiogram, reduced ejection fraction, heart failure, clinical decision support, early diagnosis, primary care
- MeSH terms
- Adolescent, Adult, Aged, Algorithms, Artificial Intelligence, Echocardiography, Electrocardiography, Female, Heart Failure, Humans, Male, Middle Aged, Stroke Volume, Decision Support Systems, Clinical, Early Diagnosis, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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