The exceptionally rapid development of highly flexible, reusable artificial intelligence (AI)
models is likely to usher in newfound capabilities in medicine. We propose a new paradigm …

A growing number of artificial intelligence (AI)-based clinical decision support systems are
showing promising performance in preclinical, in silico, evaluation, but few have yet …

Abstract Machine-learning models for medical tasks can match or surpass the performance
of clinical experts. However, in settings differing from those of the training dataset, the …

Abstract Machine learning (ML) and artificial intelligence (AI) algorithms have the potential to
derive insights from clinical data and improve patient outcomes. However, these highly …

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 …

In this review, we address the issue of fairness in the clinical integration of artificial
intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a …

Distribution shifts—where the training distribution differs from the test distribution—can
substantially degrade the accuracy of machine learning (ML) systems deployed in the wild …

Abstract Machine learning-based clinical decision support tools for sepsis create
opportunities to identify at-risk patients and initiate treatments at early time points, which is …

Despite the great promise that machine learning has offered in many fields of medicine, it
has also raised concerns about potential biases and poor generalization across genders …

Stroke ranks among the leading causes for morbidity and mortality worldwide. New and
continuously improving treatment options such as thrombolysis and thrombectomy have …