Gary Kaiser, Ph.D., and Dr. William Shea[email protected]CT3D development outpaces facilities' readinessAll clinical indications point to the value of 3D processing and image interpretation. The trick is to do it efficiently, according to Gary Kaiser, Ph.D., and Dr. William Shea, who discuss the challenges and considerations for successful 3D imaging.June 22, 2008Page 1 of 1Top StoriesRadiologic TechnologistPetition seeks to increase number of attempts for taking ARRT examThe petition has received nearly 10,000 signatures as of July 15.LegislationSocieties applaud introduction of Patients First ActEconomics2027 MPFS proposed rule would lift radiology reimbursementMRIMRI could replace DRE for prostate cancer stagingMRIPetition asks FDA to implement new OEM rule for MRI safety
![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
