In the next-generation operating room interconnected sensors will collect data, analyse it in real-time and make it available to digital assistance functions.
Researchers have shown that federated learning is successful in the context of brain imaging, by being able to analyze MRI scans of brain tumor patients and distinguish healthy brain tissue from cancerous regions.
Newer concepts like edge computing are regularly discussed alongside the cloud within the healthcare sector, often as if they are each exclusive approaches to infrastructure. However, using one does not eradicate the ability to utilise the other.
Using a computer algorithm, scientists at Uppsala University have identified a promising new treatment for neuroblastoma.
An AI platform can analyze genomic data extremely quickly, picking out key patterns to classify different types of colorectal tumors and improve the drug discovery process.
Researchers show that deep learning algorithms perform similar to human experts when classifying blood samples from patients suffering from acute myeloid leukemia.
A team of experts led by two University of Michigan researchers calls for attention to this shadow record.
Researchers use the analogy of raindrops on the sidewalk to explain their new method to identify genetic variations that cause severe pediatric diseases.
The use of blockchain will change paradigm towards patient-centered healthcare.
“A central pillar of blockchain is trust, because data cannot be altered,” says Dr Eberhard Scheuer, Chairman of the Health Information Traceability Foundation.
Australian eHealth strategy pushes towards a digital health services ecosystem which will lead to greater involvement and responsibility for consumers.
Google set it sigths to transform the healthcare industry through the use of cloud technologies and machine learning.
How University of Alberta health scientists are helping fulfil the promise of big data to revolutionize everything from prevention to diagnosis to treatment.