Abstract
Artificial intelligence (AI)-based screening tools show promise for early identification of chronic liver disease (CLD), yet their effectiveness in real-world settings may depend on clinician response to AI-generated recommendations. We performed a post hoc analysis of the intervention arm of the pragmatic, cluster-randomized DULCE trial, in which primary care clinicians received electrocardiogram-based machine learning (ECG-ML) alerts indicating elevated risk for CLD. Clinicians were categorized as high engagement (HE; top quartile) or low engagement (LE), and diagnostic yield was defined as the proportion of ECG-ML-positive cases with confirmed CLD. Among 110 clinicians receiving ≥1 alert (1385 ECG-ML-positive patients), overall engagement was 29.8%. HE was associated with higher detection of advanced CLD (OR 2.12, 95% CI 1.36–3.30; p = 0.001) and any CLD (OR 2.59, 95% CI 1.83–3.68; p < 0.001) compared with LE. Diagnostic yield was 10.6% versus 2.9% for advanced CLD and 22.3% versus 5.0% for any CLD in HE versus LE (OR 2.99, 95% CI 1.73–5.16; p < 0.001 and OR 3.74, 95% CI 2.44–5.75; p < 0.001, respectively). These findings suggest that the effectiveness of AI-based screening may depend not only on algorithm performance but also on clinician engagement with AI recommendations and highlight the importance of accounting for engagement when designing and interpreting AI-enabled clinical trials. ClinicalTrials.gov NCT05782283.
Similar content being viewed by others
Acknowledgements
Mayo Clinic MAX Innovation Award, UL1TR002377. D.A.S. is funded by National Institute of Health AA26974 and DK130181. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The ECG-enabled machine-learning model for detection of advanced chronic liver disease 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. serves on the scientific advisory board of Anumana and acts as a consultant for Anumana, AliveCor, and XAI.health. P.A.F. serves on the scientific advisory board of Anumana. D.A.S. has consulted for Mallinckrodt, Evive, Resolution Therapeutics, BioVie, AstraZeneca, Iota and PharmaIN. The other authors declare no competing financial or non-financial interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Calleri, A., Yazarkan, Y., Liu, K. et al. Clinician engagement shapes the impact of AI-based ECG screening for chronic liver disease in primary care. npj Digit. Med. (2026). https://doi.org/10.1038/s41746-026-02718-y
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41746-026-02718-y
