Open science requires trust and rigour: a framework for responsible evaluation of shared AI-ECG tools

作者：Lovedeep Singh Dhingra, Philip M Croon, Evangelos K. Oikonomou, Rohan Khera · 发表于：European Heart Journal - Digital Health · 年份：2026 · DOI：10.1093/ehjdh/ztag057 · 被引用次数：2 · 研究领域：Explainable Artificial Intelligence (XAI)、Artificial Intelligence in Healthcare and Education、ECG Monitoring and Analysis

We read with concern the study by Babur Guler and colleagues, which evaluated three AI-enhanced electrocardiography (AI-ECG) tools in 681 patients with confirmed hypertrophic cardiomyopathy (HCM).1 These tools include one for detecting underrecognized HCM, PRESENT-SHD, for screening for structural heart disease (SHD), and a multilabel rhythm and conduction classifier (ECGDx). These models developed by our group are freely available for research use on our lab’s website.2–5 We are committed to open and transparent research and welcome independent external evaluations. We agree with the authors that independent assessment of AI tools in disease-specific populations can inform understanding of the model’s behaviour in specific clinical scenarios, which is essential for the field. However, responsible external validation carries a reciprocal responsibility: Models must be represented accurately with respect to their intended use case, validated operating thresholds, and prior evidence base, and evaluated using an appropriate study design. On several of these fronts, the current study falls short in ways that undermine its conclusions. First, and most fundamentally, the study includes no control or reference group. All three tools are discriminative classifiers, developed and validated in mixed case-control populations. Applying them to a cohort composed entirely of confirmed HCM patients makes it mathematically impossible to assess diagnostic performance. The authors observe that...