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Artificial intelligence (AI) has been one of the biggest stories in healthcare for years, but many clinicians still remain unsure about how, exactly, they should be using AI to help their patients. A new analysis in European Heart Journal explored that exact issue, providing cardiology professionals with a step-by-step breakdown of how to get the most out of this potentially game-changing technology.

Canadian researchers working with Toronto General Hospital-University Health Network have developed a natural language processing (NLP) approach to predicting downstream radiology resource utilization, according to work published in the Journal of the American College of Radiology March 2.

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The FDA on March 7 granted Breakthrough Device designation to Paige.AI, a year-old New York startup that’s using AI to help diagnose and treat cancer, the company announced.

The rapid advancement of AI technologies has left the medical community in a state of flux, unsure of where to direct their efforts to deliver the best, most effective care. But to one dean the answer is simple: prioritize patients.

Data alone can’t bring in new partnerships and build a successful healthcare operator. There has to be a story behind the data to help get new models of care off the ground, according to partners with PwC, who spoke at ACHE's Congress on Healthcare Leadership conference in Chicago.

Deep learning is an important element of AI that’s helping advance diagnostics and treatment, but it also remains relatively uncharted territory.

CMS is looking for public input on how to regulate and operate the sale of health insurance coverage across state lines. The agency published a request for information (RFI) on March 6 with a public comment period of 60 days.

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The approval, made as part of a special FDA pilot program, took just 50 days.