RSNA
In the last 5 years, Artificial intelligence (AI) techniques known as deep learning have delivered rapidly improving performance in image recognition. It is anticipated that the implementation of AI in radiology over the next decade will significantly improve the quality, value, and depth of radiology's contribution to patient care and population health, and will revolutionize radiologists' workflows. Therefore the RSNA AI Webinar Series has been developed to help Radiologists understand the fundamentals of this emerging technology.
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CyberMDX
While the proliferation of smart medical devices has unleashed new healthcare capabilities, it's also created real challenges to sustained network security. It's important that security be considered at the earliest stages and built in to the medical technology research and requisition process. Unfortunately, this is rarely the case. Even so, it's critical that this problem be tackled with gusto and an organization-wide perspective.
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SAS
If you know that you need to get an AI project off the ground quickly, whether it’s due to competitive pressures, to be able to move with greater agility and speed or for testing out different AI scenarios for customer experiences – then deploying the solution via Cloud technologies is probably for you.
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Demand forecasting is not a new concept in the supply chain. In fact, it’s often referred to as ‘guessing between the goalposts,’ and for the most part, that was right.
Now, using algorithms that not only predict demand to drive forecasts but also incorporate external data sources and continuously learn and optimize the algorithm without intervention we have the ability to significantly reduce the size of those goal posts. Using AI we can improve the accuracy of our forecasts and then allow us to predict where and when those products should be deployed ahead of customer demand. The result? Improved forecasts, reduced freight costs, better customer service levels, and optimized working capital.
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