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Artificial Intelligence Homepage

NYU releases biggest ever MR data set in AI Facebook collaboration With fastMRI, acceleration of imaging by factor of four 'already possible'

Subtle Medical closes RSNA with CE mark and FDA clearance of PET AI solution Speeds up scans by factor of four, enhanced image quality

Infervision showcases new AI concepts at RSNA Detecting four different conditions on one chest scan

Canon debuts AI for image reconstruction and 1.5T MR at RSNA Advanced Intelligent Clear-IQ Engine and Vantage Orian

Where will AI make its first major market impact in radiology? Four radiology experts share their views at RSNA

Aidoc and ACR announce partnership for AI in imaging Establishing a registry to better understand AI in the clinical setting

Arterys touts cloud-native platform and regulatory approval in 98 countries AI capabilities with 'unmatched' security

NVIDIA announces AI partnerships with OSU, NIH Using Clara to deploy algorithms designed in-house

How will radiologists access AI? Integrating machine learning into existing business structure and radiologist workflow

Siemens previews new AI solutions to help radiologists and improve patient care Enhancing chest CT reads and clinical decision support

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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