Artificial Intelligence

The American College of Radiology (ACR) Data Science Institute (DSI) has launched the ACR AI-LAB, a free software platform that will help radiologists collaborate to create, validate and use AI. The college also announced it is partnering with GE Healthcare on the ACR AI-LAB.

Machine learning has the potential to reshape the patient-doctor relationship, according to a new review published in the New England Journal of Medicine, but it must overcome a few challenges first.

A new AI model can accurately determine a patient’s five-year cancer risk based on a single breast MR image, outperforming state-of-the art risk assessment models.

An AI platform designed to quickly read an x-ray and determine the manufacturer and model of a cardiac rhythm device may quicken treatment in the event of device failure.

The algorithm was externally validated on 486 normal chest radiographs and 529 abnormal chest radiographs taken from four different institutions across multiple continents.

The FDA announced Tuesday, April 3, that it is working on a new framework to regulate AI-based medical devices that continually learn from healthcare data.

Our findings show that AI-assistance can effectively improve contouring accuracy and reduce intra- and interobserver variation and contouring time, which could have a positive impact on tumor control and patient survival,” wrote authors of a recent study published in Radiology.

To improve transparency, black box algorithms are increasingly being built with functions that explain their diagnostic findings. But a recent NPR report examined how this isn’t always effective, and why a different approach to creating algorithms may be the answer.

Google’s AI research group has shown that deep-learning algorithms can fine-tune ophthalmologists’ diagnosis of diabetic retinopathy on retinal fundus photographs, according to a study slated for publication in Ophthalmology. In the study, the physicians using the algorithm bested both AI alone and unassisted physicians on accuracy.

When manually corrected by radiologists, an AI system for automatically detecting and segmenting colorectal metastases in the liver can improve interpretative efficiency, according to a study published online March 13 in Radiology: Artificial Intelligence.

Radiology patients are confident artificial intelligence will improve healthcare workflow and efficiency, but they’re skeptical of the tech itself and remain unsure of how AI will factor into the patient experience, according to a study published online March 14 in the Journal of the American College of Radiology.

Authors of the research, published in the Korean Journal of Radiology, analyzed 516 published studies and found only six percent (31 studies) externally validated their AI.