Simulation-Based Functional Assessment of a Single Commercial AI-Assisted ECG Workflow: A Pre-Implementation Proof-of-Concept Study

Commercial artificial intelligence (AI)-assisted electrocardiography systems require local assessment of the acquisition-to-review pathway before patient-facing use. We conducted a single-site, simulation-based functional assessment of one commercial 12-lead ECG configuration. A physiological signal simulator, ECG acquisition unit, AI-assisted review platform, multiparameter monitor, and central monitoring system were used to examine 13 predefined rhythm and conduction categories, alarm behavior, local export pathways, a representative five-lead signal-display subset, and formative expert feedback. Expected category-level functional agreement was observed in 12 of 13 predefined categories. In the retained record, the discordant asystole scenario was categorized as lead-off/electrode disconnection, identifying a signal-integrity boundary that requires verification of electrodes, leads, cables, waveforms, and context before clinician adjudication. Signal-to-noise ratio and root-mean-square error values from 90 derived windows in five leads were reported descriptively. These findings cannot be generalized to the seven unmeasured diagnostic leads or to complete 12-lead signal fidelity. Local PDF, HL7 v2.x, DICOM, and platform synchronization were demonstrated without independent conformance or semantic-integrity testing. Formative input from six purposively selected experts comprised 30 ordinal ratings (median 5; observed range 4–5) and qualitative comments. These data do not constitute usability or educational-effectiveness validation. The study provides bounded functional evidence for the tested configuration and identifies requirements for prospective technical, human-factors, multivendor, and patient-level validation before clinical deployment.