Importance Despite the potential of machine learning to improve multiple aspects of patient
care, barriers to clinical adoption remain. Randomized clinical trials (RCTs) are often a …

Introduction Artificial intelligence (AI) and machine learning (ML) are rapidly evolving fields
in various sectors, including healthcare. This article reviews AI's present applications in …

The recent monkeypox outbreak has become a public health concern due to its rapid spread
in more than 40 countries outside Africa. Clinical diagnosis of monkeypox in an early stage …

While a growing number of machine learning (ML) systems have been deployed in clinical
settings with the promise of improving patient care, many have struggled to gain adoption …

There has been a surge of interest in artificial intelligence and machine learning (AI/ML)-
based medical devices. However, it is poorly understood how and which AI/ML-based …

Data-driven machine learning (ML) has emerged as a promising approach for building
accurate and robust statistical models from medical data, which is collected in huge volumes …

A knowledge gap persists between machine learning (ML) developers (eg, data scientists)
and practitioners (eg, clinicians), hampering the full utilization of ML for clinical data …

Big data and machine learning are having an impact on most aspects of modern life, from
entertainment, commerce, and healthcare. Netflix knows which films and series people …

Abstract Background and Aims This systematic review examined healthcare students'
attitudes, knowledge, and skill in Artificial Intelligence (AI). Methods On August 3, 2022 …

Minimizing bias is critical to adoption and implementation of machine learning (ML) in
clinical practice. Systematic mathematical biases produce consistent and reproducible …