Artificial Intelligence

Researchers at the State University of New York at Stony Brook have demonstrated a deep-learning algorithm that can quickly diagnose early-stage lung cancer on CT scans by combining computerized self-trained tumor identification with engineered identification of specific tumor features.

The Southeast Regional Stroke Center at Erlanger, based out of the University of Tennessee’s Erlanger Medical Center in Chattanooga, receives referrals from more than 40 hospitals and treats more than 2,500 strokes each year. Patients are rushed to their internationally-recognized center, more commonly referred to as the Erlanger Stroke Network, both day and night, some arriving by ambulance and others by helicopter.

Artificial intelligence (AI) technologies are advancing at a rapid rate and starting to make a direct impact on breast imaging. There is still a lot of work to be done, however, before AI can truly be trusted with making decisions that may impact a patient’s survival, according to a new commentary published in the American Journal of Roentgenology.

Fujifilm Medical Solutions USA will host an educational symposium highlighting the influence of artificial intelligence (AI) and present its new AI initiative at RSNA 2018 in Chicago.

Convolutional neural networks (CNNs) trained with 20,000 labeled images can accurately classify chest x-rays as normal or abnormal, according to new findings published in Radiology. Training the CNN with an additional 180,000 images, the authors noted, only yielded “marginal” benefits.

MaxQ AI’s Accipio Ix intracranial hemorrhage (ICH) detection software has gained FDA clearance, the company announced this week.

Artificial intelligence (AI) tools should always be tested across “a wide range of populations,” according to new research published in PLOS Medicine. The authors shared this warning after seeing some models perform worse when tested on data from an outside health system. 

On average, China has two physicians for every 1,000 people and an upwards of 300 million people suffer from chronic diseases, thereby further burdening an already overworked workforce, according to a news report from digital outlet Brink Asia.

As AI-related medical devices continue to saturate the healthcare market, regulatory agencies like the FDA are struggling to keep up with a new category of technology.

A deep learning algorithm developed using imaging data from more than 1,000 Alzheimer’s disease (AD) patients can accurately predict the presence of AD more than six years before a doctor finalizes a diagnosis, researchers reported in Radiology Nov. 6.

Artificial intelligence (AI) can help predict how a breast tumor will respond to neoadjuvant chemotherapy (NAC), according to new findings published in the Journal of Digital Imaging.

On Oct. 26, the American College of Radiology Data Science Institute (ACR DSI) announced the release of standardized artificial intelligence (AI) use cases designed to improve AI adoption in radiology. Why, exactly, are these use cases so vital to the specialty?