The Cochrane database of systematic reviews
Oct 22, 2020
BACKGROUND: Electromechanical- and robot-assisted gait-training devices are used in rehabilitation and might help to improve walking after stroke. This is an update of a Cochrane Review first published in 2007 and previously updated in 2017.
Medicine is at a disciplinary crossroads. With the rapid integration of Artificial Intelligence (AI) into the healthcare field the future care of our patients will depend on the decisions we make now. Demographic healthcare inequalities continue to p...
Traditionally, machine learning algorithms relied on reliable labels from experts to build predictions. More recently however, algorithms have been receiving data from the general population in the form of labeling, annotations, etc. The result is th...
BACKGROUND: Doubly robust estimation produces an unbiased estimator for the average treatment effect unless both propensity score (PS) and outcome models are incorrectly specified. Studies have shown that the doubly robust estimator is subject to mor...
Machine learning methods have been employed to make predictions in psychiatry from genotypes, with the potential to bring improved prediction of outcomes in psychiatric genetics; however, their current performance is unclear. We aim to systematically...
BACKGROUND: Detection of COVID-19 cases' accuracy is posing a conundrum for scientists, physicians, and policy-makers. As of April 23, 2020, 2.7 million cases have been confirmed, over 190,000 people are dead, and about 750,000 people are reported re...
Proceedings of the National Academy of Sciences of the United States of America
May 26, 2020
Artificial intelligence (AI) systems for computer-aided diagnosis and image-based screening are being adopted worldwide by medical institutions. In such a context, generating fair and unbiased classifiers becomes of paramount importance. The research...
The use of machine learning (ML) in medicine is becoming increasingly fundamental to analyse complex problems by discovering associations among different types of information and to generate knowledge for medical decision support. Many regulatory and...
OBJECTIVE: To systematically examine the design, reporting standards, risk of bias, and claims of studies comparing the performance of diagnostic deep learning algorithms for medical imaging with that of expert clinicians.
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