Search Tag: machine learning
Teaching radiologists machine learning - AI
2018 11 Dec
Radiology is being transformed by the exponential growth of machine learning and continuously emerging technologies like deep learning, part of the artificial intelligence (AI) revolution in the imaging field. Medical imaging and operations applications are transformed as new methods and algorithms are introduced into radiology’s daily practice. ... Read more
Machine learning proving worth in readmission risk prediction
2018 07 Dec
Healthcare is seeing increased use of machine learning tools, which allows medical professionals to do their jobs more efficiently. A prime example is how machine learning has helped improve accuracy of a health system's readmission risk prediction model, resulting in a significant reduction in case managers' work hours. Bon Secours Charity Hospital's... Read more
Can AI and machine learning augment the effectiveness of a cardiologist?
2018 06 Dec
Artificial intelligence and machine learning are expected to revolutionise healthcare in the years to come. Cardiology is no exception. It is expected that AI and machine learning will provide cardiologists with a set of tools that will augment and extend their effectiveness. Some of the primary reasons why cardiologists need to embrace AI... Read more
Hacking the AI to protect imaging devices
2018 04 Dec
Radiology and medical imaging is continuously experiencing rapid enhancements including quality of patient care as a result of uncovering new aspects of the vast potential of artificial intelligence implementation in clinical practice. As more hospital medical imaging equipment connects to the internet, cyberattacks and malicious software vulnerabilities... Read more
Machine learning improves prediction of OHCA survival
2018 04 Dec
Cardiac arrest, a leading cause of admission to the intensive care unit (ICU), is associated with high mortality. Current illness severity scores perform poorly in predicting survival for this patient group. New research from Australia shows machine learning (ML) techniques can significantly increase the accuracy of estimating survival for ICU patients... Read more
Radiology’s future: outside the box– literally
2018 02 Dec
The tight roped tug of war concerning artificial intelligence and exponential technologies like deep learning and machine learning has been pulling at radiology for years. There are those fearing the dark side- new technologies as the ‘big bad’ machines that will rise up and exterminate the radiologist, the shining happy radiologists that see unicorns... Read more
New AWS machine learning services for healthcare customers
2018 22 Nov
Amazon Web Services (AWS) has announced that Amazon Translate, Amazon Comprehend and Amazon Transcribe are now HIPAA eligible services. Through using machine learning, AWS provider and patient clientele can leverage data insights to deliver better healthcare outcomes. AWS HIPAA eligible services enable use of the secure AWS environment... Read more
Emergency admission risk: machine learning model works well
2018 20 Nov
Machine learning can be used to analyse electronic health records and predict the risk of emergency hospital admissions, according to a new study from The George Institute for Global Health at the University of Oxford has found. You might also like: Hospital readmissions and machine learning The study is published in PLOS Medicine [open... Read more
Imaging applications of artificial intelligence
2018 07 Nov
The potential impact of artificial intelligence in radiology is impressive; vendors and major academic centres are developing a wide array of artificial intelligence applications and neural networks to aid radiologists in clinical diagnosis and clinical decision support. Artificial intelligence (AI) is one of the trending topics in medicine... Read more
Machine learning 'red dot' model for flagging abnormal radiographs
2018 14 Aug
UK researchers have demonstrated the application of a machine learning-based "red dot" model to classify chest radiographs as normal or abnormal, with 94.6% accuracy. Its application to real-world datasets may be warranted in optimising clinician workload in the face of expanding demand, according to the researchers. Their work is published in... Read more