How Do Patients Feel About Using AI in Healthcare?
New research from Yale Cancer Center provides a new understanding of patients’ views of artificial intelligence (AI) in healthcare.
Category: Machine Learning & AI
Machine Learning & AI
Latest
Machine Learning & AI
Latest
iCAD Showcasing Breast AI Platform at HIMSS Meeting
iCAD Inc announces it will be showcasing its Breast AI solutions, including ProFound AI for Digital Breast Tomosynthesis, in its HIMSS booth.
Machine Learning & AI
Latest
AI Breakthrough Could Revolutionize How We Research Dinosaur Fossils
A new study tests the ability of deep neural networks to properly classify segments of CT scans from three embryonic skulls previously discovered in the Gobi Desert.
Using AI to Produce CT Images—Without Radiation
A team lead by Korean researchers has developed an artificial intelligence technology that is able to generate CT images based on MRI images.
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Using Artificial Intelligence to Detect Colorectal Cancer
A Tulane University researcher found that artificial intelligence can accurately detect and diagnose colorectal cancer from tissue scans as well or better than pathologists, according to a new study in the journal Nature Communications.
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Six Things to Consider for a Successful Cloud Imaging Migration for Your Radiology Department
Medical imaging is moving to the cloud. That means the use of AI to improve clinical outcomes for patients is inevitable. Tracy Byers, senior vice president and general manager of enterprise imaging at Change Healthcare, says, “We’re thrilled to be bringing a feature-rich, highly secure, zero-footprint, cloud-native PACS, and our natively built AI solutions to market.”
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The Rise of AI in Medical Imaging Devices
Here, Ron Muscosky, worldwide product line manager at Carestream Health, sits down with AXIS Imaging News to discuss the surge of artificial intelligence technologies in medical imaging devices and what it means for clinicians—and the future of radiology.
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Technique Predicts Tumor Response to Cancer Therapy
A public/private collaboration led by researchers at The University of Texas at Austin has resulted in a new mathematical modeling technique that can accurately predict the response of tumors in breast cancer patients to treatments such as chemotherapy soon after treatment initiation.
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