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.