Seventy-seven percent of imaging professionals said machine learning is important, according to a recent report by Reaction Data. The findings signal an increase in use and understanding of machine learning among imaging professionals in the healthcare industry.

Researchers from the Massachusetts General Hospital (MGH) in Boston designed a deep-learning algorithm that provides the reasoning behind its decisions, which could help solve transparency issues associated with AI, according to a report by Health Imaging.

An AI-driven approach for detecting an early sign of diabetic retinopathy achieved an accuracy rate of more than 98 percent, according to a study published in Computers in Biology and Medicine. The results could mean a quicker and cheaper solution for diagnosing the disease earlier and possibly prevent loss of eyesight.

Fimmic, a Finland-based medical software company, is offering researchers and pathologists free access to its AI platform in an effort to advance the implementation of the technology in microscopic image analysis.

The FDA has granted 510(k) clearance for technology that uses AI to enhance images from shorter scan procedures.

Cardiologs co-founder and CEO Yann Fleureau was named the European Innovator of the Year by MIT Technology Review, which also included the French native on its list of the top 35 European innovators under the age of 35.

The FDA is speeding up the review process for new software that uses AI to help radiologists diagnose chronic thromboembolic pulmonary hypertension (CTEPH), pharmaceutical company Bayer announced Dec. 3.

NYU Langone Health’s Department of Radiology is planning to release a large-scale dataset that includes more than 1.5 million MRI knee images in an ongoing effort to make MRI scans faster with AI.

A deep-learning algorithm was significantly faster and just as accurate as most radiologists in analyzing chest X-rays for several diseases, according to a study led by Stanford University researchers.

With the help of machine learning, researchers were able to train a computer to analyze breast cancer images and classify tumors accurately, according to a study published in NPJ Breast Cancer.

After receiving FDA clearance for AI software that can detect brain bleeds from CT images, MaxQ AI has announced a deal to integrate the software with medical imaging platforms.

In an interview with AI in Healthcare, JingJia Liu, chief executive officer at Wision AI, discussed the company's new machine-learning algorithm for polyp detection, what’s next and the impact of AI products in the medical industry.