Source – https://www.radiologybusiness.com/ Imaging experts have developed an artificial intelligence tool that can help predict delays in radiology turnaround times during nights and weekends, key info for quality improvement efforts. University of California, San Francisco, researchers created the machine learning model utilizing more than 15,000 CT scans. Testing the tool out, they produced solid early Read More

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Source: standardmedia.co.ke/ Artificial Intelligence (AI), sometimes called machine intelligence, is Computer systems theory and engineering capable of performing tasks that typically require human intelligence, such as visual processing, speech recognition, decision-making, and language translation. To further expand this concept of AI in the scope of radiology results in “a computer science unit dealing with the Read More

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Source: siliconcanals.com While millions of diagnostic examinations are carried out annually, chest X-rays play a vital role in diagnosing several diseases. But the usefulness of the same can be limited due to the challenges in interpretation that need thorough and rapid evaluation of 2D image depicting complex, 3D organs, and disease processes. Sometimes, major details can be Read More

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Source: tmc.edu When the X-ray was discovered at the end of the 19th century, a new medical discipline was born. Radiology became a way to study, diagnose and treat disease. Today, expertise among radiologists, radiation oncologists, nuclear medicine physicians, medical physicists and technicians includes many forms of medical imaging—from diagnostic and cancer imaging to mammography, radiation therapy, ultrasound, Read More

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Source: techstartups.com Rad AI, an artificial intelligence startup that uses machine learning to transform the practice of radiology, today announced its formal launch and a $4 million seed round led by Gradient Ventures, Google’s AI-focused venture fund. Other backers included UP2398, Precursor Ventures, GMO Venture Partners, Array Ventures, Hike Ventures, Fifty Years VC and various angels. Rad Read More

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Source: healthitanalytics.com To advance the use of machine learning in medical imaging, researchers will have to examine radiologists’ perceptions of the technology, as well as the cost-effectiveness of these tools, according to a study published in JMIR Medical Informatics.  Countless studies have shown the diagnostic accuracy of machine learning tools. Organizations across the care continuum, from research universities to companies Read More

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Artificial Intelligence