The 5 Most Popular Radiology Comics of 2014

In a new podcast episode, Joseph Cavallo, MD, MBA, offered his perspective on AI integration, potential efficiency gains, navigating post-deployment monitoring and how to incorporate AI literacy into radiology resident training.

The FFR-IQ software recently demonstrated a 92.2 percent sensitivity for non-invasive estimation of fractional flow reserve (FFRC) based on coronary CT angiography (CCTA) scans.

A Kaiser score > 5 with breast MRI demonstrated a 97 percent sensitivity and 87.9 percent specificity for predicting malignancy in patients with BI-RADS 0 or 4 presentations on mammography and breast ultrasound, according to a new study.

In a recent interview, Francesco Giganti, MD, PhD, discussed findings from a recent multicenter study demonstrating the ability of an AI software to provide significant clarity to initial PI-RADS 3 assessments on MRI and facilitate a nearly 19 percent reduction in unnecessary prostate biopsies.

The NEXT-Cue AI software, which incorporates analysis of brain MRI scans, reportedly facilitates prognostic assessments of potential progression from mild cognitive impairment (MCI) to Alzheimer’s disease within five years.

In a new podcast episode, Amir Iravani, MD, FRACP, discusses emerging dosimetry data from the COMPETE trial for 177Lu-edotreotide and how it may help facilitate more personalized treatment for patients with gastroenteropancreatic neuroendocrine tumors (GEP-NETs).

In a recent interview, Phillip Koo, MD, discussed emerging trends with AI, theranostics and empowered patients, and how these developments may lead to challenges and potential opportunities for radiologists and nuclear medicine physicians.

AI has to do more than show potential. It has to deliver measurable results.The Rad AI Outcomes and Market Impact Compendium brings together real-world clinical, operational and financial evidence from health systems, academic medical centers and radiology practices using Rad AI Reporting, Impressions and Continuity.Explore results including 60+ minutes saved per radiologist shift, a 47% reduction in report error rates, 70%+ patient follow-up adherence and >7x ROI — plus, case studies showing how organizations are translating AI into greater capacity, quality and financial performance.See what AI built specifically for radiology can deliver in practice.