Artificial Intelligence

Extracting radiomic features from MR images can help radiologists distinguish between benign breast lesions and luminal A breast cancers, according to a new study published by Academic Radiology.

It’s one of the most frequently discussed questions in radiology today: What kind of long-term impact will artificial intelligence (AI) have on radiologists?

Thought leaders within radiology largely agree that the specialty is in a unique position to help lead the implementation of artificial intelligence (AI) into clinical practice. But how, exactly, does that happen?

In a profession where success hinges on quality imaging surveillance, a lack of universally accepted guidelines often gives way to inconsistent reporting in radiology rooms—but a group of clinicians thinks artificial intelligence could be cleaning up workflow.

Charles E. Kahn Jr., MD, MS, professor and vice chair of the department of radiology at the University of Pennsylvania’s Perelman School of Medicine in Philadelphia, has been named the editor of RSNA’s new online journal, Radiology: Artificial Intelligence.

Automated and clinical breast density evaluation methods are equally accurate in predicting a patient’s risk of breast cancer, according to a new study published in Annals of Internal Medicine.

Anant Madabhushi, PhD, a professor at Case Western Reserve University in Cleveland, has led significant deep learning research in recent years, but he doesn’t necessarily think this evolving technology will replace radiologists and pathologists any time soon.

The American College of Radiology (ACR) Data Science Institute (DSI) and Society for Imaging Informatics in Medicine (SIIM) are joining forces on May 30 to host the Spring 2018 Data Science Summit: Economics of Artificial Intelligence (AI) in Health Care.

Deep learning-based convolutional neural networks (CNNs) can help radiologists select musculoskeletal MRI protocols, according to a study published by the Journal of Digital Imaging.

The FDA is working to encourage the use of artificial intelligence (AI) technologies in healthcare, according the prepared remarks by the agency’s commissioner, Scott Gottlieb, MD, at Health Datapalooza in Washington, D.C.

The profession of radiology may rightly regard 2017 as an extended coming-out party for AI within the specialty. At ACR’s annual meeting in May, the keynote speeches all revolved around the changes AI will bring. AI occupied an entire quadrant of space, including a dedicated stage, at the RSNA annual meeting in the fall. Seemingly dozens of startups, along with numerous established companies, lined up in vendor booths ready to dazzle you with the next generation of radiology technology.

With few exceptions, the most attention-demanding discussions about how and when artificial intelligence will transform radiology have been led by—and largely held within—the academic sector. That’s not surprising, given that teaching radiologists are the ones doing the research, blazing the trails and comparing the notes.