A COGNITIVE RISK TAXONOMY FOR AI-ASSISTED EMERGENCY ECG INTERPRETATION: MAPPING ALGORITHMIC ERROR TO PHYSICIAN BIAS PATHWAYS

Background: Artificial intelligence algorithms for electrocardiogram (ECG) interpretation are now standard in emergency departments worldwide, yet the assumption that AI and physician errors are complementary — and therefore self-correcting — has never been systematically tested against the cognitive realities of emergency medicine practice.

Objective: To develop a cognitive risk taxonomy that maps specific AI error patterns through specific physician bias pathways to specific clinical risk predictions for emergency-critical ECG diagnoses.

Methods: We analysed 21,799 dual-labelled ECGs from the PTB-XL+ dataset, comparing expert cardiologist annotations against GE 12SL algorithm output across 54 emergency-critical SNOMED CT concepts spanning acute myocardial infarction, life-threatening arrhythmias, conduction blocks, and ischaemia. A five-stage disagreement analysis framework quantified error direction, magnitude, confidence profiles, compound co-occurrence patterns, and diagnostic substitution profiles. Each disagreement signature was mapped to cognitive bias pathways derived from dual-process theory and scored on a composite of frequency, clinical severity, and bias amplification potential.

Results: Of 106,401 non-trivial comparisons, 93.7% were discordant (6.3% agreement), with 81% of the dataset carrying at least one emergency-critical disagreement (mean 5.7 per affected ECG). Eight named disagreement signatures were identified, organised into a two-tier, four-class taxonomy: Lethal Diagnosis Miss (anteroseptal MI blind spot, SVT/VT inversion, LBBB–STEMI mask), Mechanism Blindness (bradycardia inflation, fascicular desert), Signal Corruption (ischaemia noise floor, old MI avalanche), and Self-Undermining AI (QT alarm fatigue). Seven were classified as CRITICAL risk. The AI’s binary confidence architecture delivered 81% of overcalls at maximum confidence with no hedging, creating near-maximum anchoring potential for every error. Diagnostic substitution profiling revealed that AI misses are not silent omissions but active reframings: when the AI misses anteroseptal MI, it labels “Old MI” on 60.6% of those ECGs; when it misses ventricular tachycardia, it labels “SVT” on 45.8%.

Conclusions: The Compound Risk Hypothesis was validated across all eight danger zones: in every case, the AI–physician combination was projected to perform worse than the physician alone through three mechanisms — Direct Suppression, Capability Destruction, and Environmental Contamination. A seven-component safeguard framework targeting class-specific bias pathways was developed, designed to be net-negative on alert burden. The taxonomy framework is system-agnostic and designed for reapplication to any AI system with dual-label validation data.