Artificial Intelligence

Artificial intelligence (AI) is becoming a crucial component of healthcare to help augment physicians and make them more efficient. In medical imaging, it is helping radiologists more efficiently manage PACS worklists, enable structured reporting, auto detect injuries and diseases, and to pull in relevant prior exams and patient data. In cardiology, AI is helping automate tasks and measurements on imaging and in reporting systems, guides novice echo users to improve imaging and accuracy, and can risk stratify patients. AI includes deep learning algorithms, machine learning, computer-aided detection (CAD) systems, and convolutional neural networks. 

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FDA awards breakthrough designation to AI-powered tuberculosis diagnostic tool

A rising number of patients in the U.S. are diagnosed with TB, and AI is being deployed to turn the tide.

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Harvard professor accused of doctoring images used in decades of research

A total of 28 studies have been flagged as potentially fraudulent, with some published as early as 2001.

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Radiology AI marketplace CARPL raises $6M in seed funding

Software investor Stellaris Venture Partners led the funding round, with additional contributions from UnitedHealth Group, Bain & Co. and LeapFrog Investments.   

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AI spots missed findings on chest X-rays, aiding nonradiologists in emergency setting

Emergency units often lack 24/7 coverage by radiologists, presenting an opportunity for AI to aid in diagnostics.

healthcare technology safety hazards ECRI

ECRI: Top 10 healthcare technology hazards that are avoidable (by suppliers) and/or minimizable (by providers)

The nonprofit patient-safety organization says the list “reflects our judgment about which risks should be given attention now to help care providers, as well as device manufacturers, prioritize their patient safety efforts.”

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AI-powered risk score predicts how heart failure patients will respond to loop diuretics

New research out of Texas could go a long way toward improving care for patients with acute decompensated heart failure.

Hip strain injury fracture broken hip

AI model predicts hip fractures in a short-term timeframe

Current risk assessments are unable to calculate the likelihood of a near-future fracture after a patient breaks their hip.

Jeremy Slivnick, MD, presents at the American Society of Echocardiography (ASE) 2023 meeting on how artificial intelligence (AI) can help make echocardiography better able to detect subtle signs of early cardiac amyloid disease when it is easier to treat with better outcomes. ssistant professor of medicine and an advanced cardiac imager at the University of Chicago.

AI models for cardiac amyloidosis could make a world of difference

Jeremy Slivnick, MD, spoke with Cardiovascular Business about AI's potential to transform how cardiac amyloidosis is diagnosed and treated.