Skin-interfaced electronics is gradually changing medical practices by enabling continuous
and non-invasive tracking of physiological and biochemical information. With the rise of big …

Background Interest in the application of machine learning (ML) to the design, conduct, and
analysis of clinical trials has grown, but the evidence base for such applications has not …

Artificial intelligence (AI) has given the electrocardiogram (ECG) and clinicians reading them
super-human diagnostic abilities. Trained without hard-coded rules by finding often …

In the recent decade, deep learning, a subset of artificial intelligence and machine learning,
has been used to identify patterns in big healthcare datasets for disease phenotyping, event …

Deep learning methods for graphs achieve remarkable performance on many tasks.
However, despite the proliferation of such methods and their success, recent findings …

Abstract Internet of Medical Things (IoMT) supports traditional healthcare systems by
providing enhanced scalability, efficiency, reliability, and accuracy of healthcare services. It …

With the advent of machine learning (ML) and deep learning (DL) empowered applications
for critical applications like healthcare, the questions about liability, trust, and interpretability …

The electrocardiogram (ECG) is the most commonly used exam for the evaluation of
cardiovascular diseases. Here we propose that the age predicted by artificial intelligence …

Artificial intelligence (AI) and machine learning (ML) in medicine are currently areas of
intense exploration, showing potential to automate human tasks and even perform tasks …

The healthcare industry generates troves of unlabelled physiological data. This data can be
exploited via contrastive learning, a self-supervised pre-training method that encourages …