… First, we review clinical AI applications that provide decision support more broadly and … a
specific clinical domain. Second, we focus on studies that reported the evaluation of clinical AI …

… Artificial intelligence focuses on creating an intelligent machine that can successfully
perform human intellectual tasks by employing complex non-linear mathematical modelling …

… of machine learning, reinforcement learning, and deep learning are well-suited to deal with
such data. The authors in this paper review current applications of AI in clinical … by clinical …

… This analysis is written with the dual aim of helping clinical … by highlighting some of the
clinical safety questions that must be … framework for considering clinical artificial intelligence (AI) …

… machine learning classifiers to distinguish patients with COVID-19 from pre-pandemic
controls, using electronic health record data for patients presenting to the emergency department …

… of patients at the emergency department is critical to inform … model that learns from routine
clinical variables. Our AI prognosis … In order to verify performance in a real clinical setting, we …

… We systematically reviewed the literature on attitudes toward clinical AI. We summarised
current knowledge and offered guidance for future studies, with the ultimate goal of supporting …

… Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence
(AI… by integrating it into clinical practice. Reporting AI’s role in clinical practice is crucial for …

… The integration of artificial intelligence (AI) will be one of the biggest transformations for …
Much of the workflow of histopathology departments has remained largely unchanged for …

… A growing number of artificial intelligence (AI)-based clinical decision support systems
are … Early stage clinical evaluation is important to assess an AI system’s actual clinical …