Artificial intelligence versus human expertise: ECG-based detection of occlusive myocardial infarction after cardiac arrest

BACKGROUND/AIM: Accurate electrocardiogram (ECG) interpretation after cardiac arrest is essential for identifying occlusive myocardial infarction (OMI), but post-resuscitation artifacts make this challenging. While artificial intelligence (AI) offers promising support, its diagnostic performance in this critical setting remains uncertain. METHODS: This single-centre study included 97 adult patients resuscitated from cardiac arrest (CA). Post-return of spontaneous circulation (ROSC) ECG were evaluated by four methods: human experts (HE), a validated deep neural network [Queen of Hearts (QoH)], and two large language model (LLM)-based AI Chatbots (AI-CB) - ChatGPT and EKG Analyst. Primary outcome was AUROC for presence and probability of OMI and acute coronary occlusion (ACO), determined by coronary angiography. RESULTS: For ACO (TIMI 0), QoH yielded highest AUROC (0.846 [0.752-0.939]), followed by HE (0.735 [0.622-0.848]). Both AI-CB resulted in lowest AUROC (ChatGPT: 0.456 [0.319-0.592]; EKG Analyst: 0.474 [0.346-0.603]). For OMI (TIMI 0-2 or TIMI 3 + peak-troponin), QoH again achieved highest AUROC (0.745 [0.647-0.843]), followed by HE (0.635 [0.515-0.755]), AI-CB were lowest again (ChatGPT: 0.495 [0.376-0.614]; EKG Analyst: 0.626 [0.508-0.743]). Threshold-dependent performance metrics revealed high sensitivity (ACO: 100 %; OMI: 98.36 %) for both AI-CB, at the cost of minimal specificity. QoH and HE showed more even distributions of sensitivity/specificity. CONCLUSION: QoH, despite operating without awareness of the CA-setting and thus likely at a relative disadvantage, and HE showed robust diagnostic accuracy. Due to undifferentiated overdiagnosis, general LLMs remain unsuitable for ECG interpretation. Domain-specific tools like QoH may offer complementary value.