In the past decade, the application of machine learning (ML) to healthcare has helped drive
the automation of physician tasks as well as enhancements in clinical capabilities and …

Purpose To assess generalizability of published deep learning (DL) algorithms for radiologic
diagnosis. Materials and Methods In this systematic review, the PubMed database was …

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 …

Machine learning (ML) systems often exhibit unexpectedly poor behavior when they are
deployed in real-world domains. We identify underspecification in ML pipelines as a key …

Background Clinical prediction models should be validated before implementation in clinical
practice. But is favorable performance at internal validation or one external validation …

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 …

The coronavirus disease 2019 (COVID-19) pandemic has imposed dramatic challenges to
health-care organizations worldwide. To combat the global crisis, the use of thoracic …

Steps to avoid overuse and misuse of machine learning in clinical research | Nature Medicine
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This editorial aims to contribute to the current debate about the quality of studies that apply
machine learning (ML) methodologies to medical data to extract value from them and …