AI News, Supporting the use of standardized nursing terminologies with ... artificial intelligence
What is Interoperability? | HIMSS
Interoperability is the ability of different information systems, devices and applications (‘systems’) to access, exchange, integrate and cooperatively use data in a coordinated manner, within and across organizational, regional and national boundaries, to provide timely and seamless portability of information and optimize the health of individuals and populations globally.
Health data exchange architectures, application interfaces and standards enable data to be accessed and shared appropriately and securely across the complete spectrum of care, within all applicable settings and with relevant stakeholders, including by the individual.
Benefits of Artificial Intelligence to Radiology Workflows
“Radiological imaging data continues to grow at a disproportionate rate when compared with the number of available trained readers, and the decline in imaging reimbursements has forced health-care providers to compensate by increasing productivity,”
Industry leaders in radiology are actively working to identify opportunities for machine learning, neural networks, and natural language processing to optimize radiology workflows.
While the workgroup noted that interpretation (e.g., detection, segmentation, classification) garnered the bulk of attention, it emphasized in a Journal of the American College of Radiology article that “progress in AI research and development is occurring in all areas.”
In this context, deep learning can potentially excel by learning a hierarchical normal representation of a specific type of image from a large number of normal exams,”
“Given its ability to learn complex data representations, deep learning is also often robust against undesired variation, such as the inter-reader variability, and can hence be applied to a large variety of clinical conditions and parameters,”
“In many ways, deep learning can mirror what trained radiologists do, that is, identify image parameters but also weigh up the importance of these parameters on the basis of other factors to arrive at a clinical decision.”
“We find a widening gap between advancements in image acquisition hardware and image-reconstruction software, a gap that can potentially be addressed by new deep learning methods for suppressing artefacts and improving overall quality,”
“The choice of protocol, however, is subject to variability since it is frequently operator-dependent, and consequently, the radiation dose and the quality of the exam are subject to variability at both intra- and inter-institutional levels,”
“In this setting, AI can be an optimising tool for assisting the technologist and radiologist in choosing a personalised patient’s protocol, in tracking the patient’s dose parameters, and in providing an estimate of the radiation risks associated with cumulative dose and the patient’s susceptibility (age and other clinical parameters).”
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- On 15. april 2021
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