Dr. Christopher Austin[email protected]IS3 questions to shape the future of AI and radiologyThe introduction of disruptive healthcare technologies rarely threatens the role of the clinician -- but radiology is fast becoming the exception. In this column, Dr. Christopher Austin of GE Healthcare suggests three questions that radiologists should ask themselves if they want to lead and not follow the changes that are coming.November 15, 2016Page 1 of 1Top StoriesAIRadiology dominates thirty years of FDA AI device approvalsThe total annual authorization volume increased from a mean of 1.8 per year between 1995 and 2014 to 264 per year between 2023 and 2025.Radiologic TechnologistPetition seeks to increase number of attempts for taking ARRT examLegislationSocieties applaud introduction of Patients First ActEconomics2027 MPFS proposed rule would lift radiology reimbursementMRIMRI could replace DRE for prostate cancer staging
![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)
