A Novel real-time arrhythmia detection model using YOLOv8

High accuracy achieved but validation scope unclear: The model reports 99.5% accuracy on the MIT-BIH dataset, but the paper doesn't specify whether this was tested on diverse patient populations or only on the specific dataset used for training, raising questions about real-world performance across different demographics and heart conditions.

Speed enables home monitoring but raises safety questions: The model detects arrhythmias in 0.002 seconds, making home-based monitoring feasible, but the paper doesn't address critical responsible AI concerns such as what happens when the system makes errors, how users should respond to alerts, or whether medical oversight is required before deployment.

Explainability promised but not yet delivered: While the authors mention plans for "explainable AI (XAI)" capabilities, the current model lacks transparency about why it classifies specific ECG patterns as particular arrhythmias—a significant gap for responsible healthcare AI, since doctors and patients need to understand the reasoning behind medical alerts.

Dataset limitations may affect equity: The model is trained exclusively on the MIT-BIH dataset, which may not represent diverse populations, potentially leading to disparities in detection accuracy across different demographic groups—an important consideration for equitable healthcare technology.

Clinical validation and regulatory pathway unclear: The paper doesn't discuss whether the system has undergone clinical trials, received regulatory approval, or established protocols for integration with existing healthcare systems, which are essential steps before deploying AI in actual patient care.