Overcoming Normalcy Bias in Acute Myocardial Infarction: A Case Report of Generative AI as a Behavioral Catalyst for Emergency Care Seeking

Abstract

Pre-hospital delay remains a major determinant of outcomes in acute myocardial infarction (AMI), with normalcy bias playing a central role in patients' failure to interpret symptoms as signals of serious illness. This case report examines the role of generative artificial intelligence (AI) not as a diagnostic instrument but as a behavioral catalyst that prompted timely emergency care seeking. A man in his early sixties presented with chest discomfort, neck radiation, bilateral lower molar pain, diaphoresis, and cold extremities. Although these symptoms are medically typical of AMI, myocardial infarction was not part of the patient's immediate cognitive framework, and they were initially interpreted as dental discomfort or nonspecific physical fatigue. After consulting a publicly available generative AI system that issued a clear imperative to contact emergency medical services, the patient activated emergency care. He was subsequently diagnosed with inferior ST-elevation myocardial infarction due to right coronary artery occlusion and underwent successful emergency percutaneous coronary intervention. This case suggests that AI-generated language can mitigate normalcy bias and accelerate patient decision-making in acute medical settings without functioning as a diagnostic tool.

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