Enhancing Diagnostic Accuracy in Dyspnea: The Role of AI-Enhanced ECG Interpretation

Dyspnea is a frequent and diagnostically challenging presentation in emergency departments, requiring rapid distinction between cardiac and non-cardiac causes. Traditional approaches, including NT-proBNP testing and clinical assessment, often lack optimal sensitivity and specificity. Recent advancements in artificial intelligence (AI) have introduced AI-enhanced electrocardiogram (ECG) analysis as a promising diagnostic tool capable of identifying cardiac dysfunction with high accuracy. A structured literature review was performed using PubMed to identify randomized controlled trials, prospective validations, and systematic reviews published between 2018 and 2024. Studies evaluating AI-ECG performance, NT-proBNP testing, and clinical assessment in adult patients presenting with dyspnea were included. Data extracted included sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) for each diagnostic method. Studies focusing on external validation of AI models and clinical applicability were prioritized to ensure relevance to real-world emergency department settings.