Beyond Text: The Impact of Clinical Context on GPT-4's 12-Lead Electrocardiogram Interpretation Accuracy

Artificial intelligence (AI) and large language models (LLMs), such as OpenAI's GPT-4, are increasingly being explored for medical applications. Recently, GPT-4 gained image processing capabilities, enabling it to handle tasks such as image captioning, visual question answering, and potentially interpreting medical data. Despite promising potential in diagnostics, the effectiveness of GPT-4 in interpreting complex 12-lead electrocardiograms (ECGs) remains to be assessed.

This study utilized GPT-4 to interpret 150 12-lead ECGs from the Cardiology Research Dubrava (CaRD) registry, spanning a wide range of cardiac pathologies. The ECGs were classified into 4 categories for analysis: arrhythmias, conduction system abnormalities, acute coronary syndrome, and other. Two experiments were conducted: one where GPT-4 interpreted ECGs without clinical context, and another with added clinical scenarios. A panel of experienced cardiologists evaluated the accuracy of GPT-4's interpretations.

In this cross-sectional observational study, GPT-4 demonstrated a correct interpretation rate of 19% without clinical context and a significantly improved rate of 45% with context (P < 0.001). The addition of clinical scenarios significantly enhanced interpretative accuracy, particularly in the acute coronary syndrome category (10% vs 70%; P < 0.0.01). The "other" category showed no impact (51% vs 59%; P = 0.640), and trends toward significance were observed in the arrhythmias (9.7% vs 32%; P = 0.059) and conduction system abnormalities (4.8% vs 19%; P = 0.088) categories when given clinical context.

Although GPT-4 shows potential in aiding 12-lead ECG interpretation, its effectiveness varies significantly with clinical context. The study suggests that GPT-4 alone in its current form may not provide accurate 12-lead ECG interpretation.