Artificial Intelligence

Machine learning and artificial intelligence (AI) are two hotly discussed topics in healthcare, but radiologists tend to fear a future where computers replace people. But that fear may be unwarranted, according to one expert.

According to survey results recently published online in the Journal of the American College of Radiology, more than one-third of radiologists lack exposure to artificial intelligence (AI) educational material and resources. Additionally, most trainees' desire to learn about or pursue diagnostic radiology is hindered by AI's role in medicine.

A University of Saskatchewan team has created a deep learning technique that demonstrated enhanced de-noising capabilities in low-dose CT (LDCT) imaging, resulting in little resolution loss and better performance, according to a study published in the Journal of Digital Imaging.

Radiation therapy is an integral part of many cancer treatments. Ideally, doses are focused on the observable tumor while leaving surrounding organs unaffected, but determining the figuration of tumors and organs-at-risk is done manually—a time consuming and, at times, imprecise task for radiologists.

A new broad oncology deep learning suite from the cloud-based medical imaging software solutions company Arterys Inc. was approved for 501(k) clearance by the FDA, according to a report by Business Insider.  

Through functional MRI (fMRI) and machine learning technology, UCLA researchers have developed a way to predict whether individuals with obsessive compulsive disorder (OCD) will benefit from cognitive behavioral therapy (CBT), according to a recent UCLA release.  

According to a Feb. 13 press release, the FDA announced clearance for the marketing of the Viz.AI Contact application, a clinical decision support software created to analyze CT results and notify providers of a potential stroke.

According to a December 2017 research survey conducted by the healthcare market research firm Reaction Data, most hospitals and imaging centers will be using machine learning or artificial intelligence (AI) technology by 2020.

As industry experts continue to explore artificial intelligence (AI) applications in radiology, the question remains of whether AI applications can and will add value, including in new knowledge and information to provide patients with better outcomes at lower costs.

Artificial intelligence and machine learning are all the rage—and for good reason. But researchers claim the brain doesn’t actually use the regions identified by machine learning to perform a task. Rather, these algorithms reflect the mental associations related to the task.

As medical imaging continues to evolve, Health Imaging spoke with enterprise imaging and health informatics expert Paul Chang, MD, professor of radiology and vice chair rad informatics at the University of Chicago Medicine, about what practitioners and healthcare technology leaders should keep in mind regarding artificial intelligence (AI) and deep learning in the coming year.  

Myelin, a fatty substance that sheaths nerves, is often damaged or missing in individuals with multiple sclerosis (MS). Physicians have depended on MR to detect demyelination, but the modality is incapable of differentiating between lost or damaged myelin and inflammation.