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<p>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; <i>p</i> = 0.001) and any CLD (OR 2.59, 95% CI 1.83–3.68; <i>p</i> &lt; 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; <i>p</i> &lt; 0.001 and OR 3.74, 95% CI 2.44–5.75; <i>p</i> &lt; 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.</p>

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Clinician engagement shapes the impact of AI-based ECG screening for chronic liver disease in primary care

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月9日

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  • Alberto Calleri,
  • Yigit Yazarkan,
  • Kan Liu,
  • Blake A. Kassmeyer,
  • Ryan J. Lennon,
  • Puru Rattan,
  • Amir Seid,
  • Matthew E. Bernard,
  • Gagandeep Singh,
  • Mark E. Deyo-Svendsen,
  • Graham King,
  • Stephen K. Stacey,
  • Amy Olofson,
  • Alina Allen,
  • Joseph C. Ahn,
  • Paul A. Friedman,
  • Patrick S. Kamath,
  • Zachi I. Attia,
  • Peter A. Noseworthy,
  • Vijay H. Shah,
  • David Rushlow,
  • Douglas A. Simonetto

摘要

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.