ArticleNPJ digital medicine2026
Clinician engagement shapes the impact of AI-based ECG screening for chronic liver disease in primary care.
Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05782283 (Early Detection of Advanced Liver Disease Via Artificial Intelligence-Enabled Electrocardiogram), which is not on this map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Early Detection of Advanced Liver Disease Via Artificial Intelligence-Enabled Electrocardiogram (Advance): A Pragmatic, Cluster-Randomized Clinical Trial
Who cites it
1 citing paper in PubMed.
- Reply to: Clinician engagement modifies effectiveness of AI enabled ECG screening.NPJ digital medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
22 authors.
Funding
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.