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Artificial Intelligence Homepage

BSWH to install Glassbeam's CLEAN blueprint to leverage machine uptime Will include integrated CMMS software by EQ2

Beyond the hype: How practical AI is enhancing radiology Insights from Imad B. Nijim, chief information officer for MEDNAX Radiology Solutions

Machine learning reduces false positives for lung cancer in low-dose CT False positives occur at rate of 96 percent

Siemens and ESR present first Digital Experience Hall at ECR Aims to stimulate exchange of thoughts and knowledge about digitization in radiology

Canon showcases CT image reconstruction tech and software upgrade at ECR Removing noise while preserving signals

Personalized cardiac test could eliminate unnecessary catheterizations Examines flow of blood with AI and CT

GE debuts work-in-progress algorithms at ECR 2019 For predicting no-shows and detecting the presence of pneumothoraces

Women's brains appear three years younger than men's at the same age: PET study A machine-learning algorithm assisted with the analysis

AI and personalized medicine to raise the bar in radiology reporting New innovations could provide myriad benefits to clinical workflow

Hologic launches Unifi Analytics to curb mammo downtime Predicting tube failures before they happen and setting performance benchmarks

Five key takeaways about AI from RSNA

by John W. Mitchell , Senior Correspondent

4 – Hospital administration cannot afford AI for the sake of AI
Administrators who pay for things in hospitals are mostly interested in immediate, low-hanging fruit. They are not interested in paying for "toys," so pitching AI for AI's sake will not be a successful strategy. Rather, radiologists must speak the language of administrators. This includes AI spending for better efficiency, reducing costs, increasing output and getting better outcomes. Start building IT and informatics infrastructure now, which is the "road" for AI to travel.

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5 – Keep AI in proper perspective.
It will not replace radiologists but augment them. AI is neither a horrible threat nor a promised savior. Radiologists need help to meet the growing burden of workloads, expectations for quality, and cost control. AI machine learning applications are a viable solution.

Radiology has always been a leader in adopting disruptive technology, and AI is no different. Radiologists should devote some of their CME learning to understanding the use of algorithms in their practice. Such training is offered through several entities, including RSNA. Chang quoted Mark Zuckerberg on a critical truth: "The best AI-powered systems require humans to play an active role in their creation, tending and operation."

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