Clinical applications of machine learning algorithms: beyond the black box | The BMJ Skip to
main content Intended for healthcare professionals Access provided by Google Indexer …

Machine learning, artificial intelligence, and other modern statistical methods are providing
new opportunities to operationalise previously untapped and rapidly growing sources of …

Steps to avoid overuse and misuse of machine learning in clinical research | Nature Medicine
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Applications of machine learning on clinical data are now attaining levels of performance
that match or exceed human clinicians. 1–3 Fields involving image interpretation …

Abstract Machine learning algorithms are being used to screen and diagnose disease,
prognosticate and predict therapeutic responses. Hundreds of new algorithms are being …

An emphasis on overly broad notions of generalisability as it pertains to applications of
machine learning in health care can overlook situations in which machine learning might …

For machine learning developers, the use of prediction tools in real-world clinical settings
can be a distant goal. Recently published guidelines for reporting clinical research that …

Machines that can learn and correct themselves already perform better than doctors at some
tasks, says Jörg Goldhahn, but Vanessa Rampton and Giatgen A Spinas maintain that …

Objective To examine the role of explainability in machine learning for healthcare (MLHC),
and its necessity and significance with respect to effective and ethical MLHC application …

Dataset Shift in Clinical Trials This letter outlines how to identify, and potentially mitigate,
common sources of “dataset shift” in machine-learning systems. This occurs when the model …