Betting on Monte Carlo: GPUs a ‘Game Changer’ for Nuking Noise in Nuclear Imaging

Andras Wirth is like many early AI researchers: His deep learning ambitions only turned into reality because of a sea change in technology. A physicist, Wirth wanted to run Monte Carlo algorithms to make leaping advances in nuclear imaging, which was previously computationally impossible without massive supercomputers. A decade ago, his breakthrough came when his Read article >

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Error Parer: How AI Could Help Cardiologists Detect Heart Defects Without Skipping a Beat

Nearly a third of physicians will be sued at least once in their careers — most commonly for an error in diagnosis. Medical errors are also the third-leading cause of death in the United States, according to a study by Johns Hopkins Medicine. Deep learning has the potential to help doctors cut down on diagnostic Read article >

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AI and Clinicians a ‘Winning Combination,’ Healthcare Luminary Eric Topol Says at GTC

Deep learning is putting the “care” back in healthcare. “It used to be we just talked about deep sequencing. Now we talk about deep everything in medicine,” healthcare luminary Eric Topol told a packed audience Tuesday morning at the 10th annual GPU Technology Conference, in San Jose. With advances in AI and healthcare, he said, Read article >

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Heart of the Matter: How AI Can Create Personalized Prosthetic Heart Valves

Heart-valve replacements can restore normal blood flow for patients with aortic valvular disease — but only when the prosthesis is a good fit. A drawback of prosthetic heart valves is that they’re manufactured in fixed sizes. In a presentation Monday at the GPU Technology Conference in San Jose, Iowa State professor Adarsh Krishnamurthy and graduate Read article >

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How an Early Bet on Digital Enables a Healthcare Network’s Suite of AI Applications

Few predicted in the mid-’90s that the deep learning revolution was coming. But Geisinger, a healthcare system serving people across Pennsylvania and New Jersey, made an early bet on digital health records. And that gave it a major advantage when implementing AI two decades later. Geisinger adopted electronic health records in 1996 and began digitally Read article >

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How AI Is Changing Medical Imaging

From images of cats to handwritten numbers, neural networks have been used for years to process visual data. Deep learning models can identify features in images, enhance image quality, and spot outliers and abnormalities. These same abilities are helping AI transform the field of radiology by saving time and money for healthcare organizations while improving Read article >

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Talk to Me: Deep Learning Identifies Depression in Speech Patterns

“Talk therapy” is often used by psychotherapists to help patients overcome depression or anxiety through conversation. A research team at Massachusetts Institute of Technology is using deep learning to uncover what might be called “talk diagnosis” — detecting signs of depression by analyzing a patient’s speech. The research could lead to effective, and inexpensive, diagnosis Read article >

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Reading the Vital Signs: Leading Minds in Medicine to Discuss AI Progress in Healthcare

The buzz around AI in medicine is contagious. From radiology and drug discovery to disease risk prediction and patient care, deep learning is transforming healthcare from every angle. You can learn about it all at the GPU Technology Conference, where healthcare innovators from industry, universities and medical institutions will gather to share how AI and Read article >

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AI Hotline: Startup Analyzes Emergency Calls to Identify Cardiac Arrest Victims

“911. What’s your emergency?” Denmark-based startup Corti knows that the stakes are high when dialing emergency services. So it built an AI tool to provide immediate feedback and guidance to emergency call responders, helping them ask the right questions and quickly identify highly acute cases. Its speech-recognition software, Corti AI, can cut down the number Read article >

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How UCSF Researchers Are Using AI on Some of Healthcare’s Toughest Problems

Hospitals produce huge volumes of medical data that, when handled with concern for privacy and security, could be used to re-examine everything from hospital administration to patient care. But this means finding careful ways to assemble this data, so it’s no longer locked in organizational silos. A research team at the University of California, San Read article >

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