Safety, reliability crucial in AI development for ECG readings

The use of artificial intelligence has been a hot topic in cardiology for the past few years. For example, deep neural networks can be used to analyze ECG tracings and may be more accurate than human experts. Even with these advantages, there may be some hesitation on completely relying on this technology. In a recent research letter published in Nature Medicine, researchers developed a way to integrate smoothed adversarial examples for single-lead ECGs. Researchers found that when subtle adversarial perturbations that are indistinguishable to the human eye were added to ECG tracings, the misdiagnosis rate of the deep learning algorithm was 74%.