Erik L. Ridley, Kate Madden Yee, Will Morton, Amerigo Allegretto, and Frances Rylands-Monk[email protected]HomeMinnies winners for 2022 feature photon-counting CT, AIMinnies winners, page 3October 27, 2022HomeMinnies winners for 2022 feature photon-counting CT, AIMinnies winners, page 2October 27, 2022HomeMinnies winners for 2022 feature photon-counting CT, AIThe 2022 Minnies awards showcase the growing momentum and clinical impact of photon-counting CT. The burgeoning technology featured in three different categories: Hottest Clinical Procedure, Best New Radiology Device, and Image of the Year.October 27, 2022Page 1 of 1Top StoriesEconomics2027 MPFS proposed rule would lift radiology reimbursementIf finalized, the proposed rule would hike reimbursement by 2% for radiology, 2% for nuclear medicine, 3% for interventional radiology, and 3% for radiation oncology.MRIMRI could replace DRE for prostate cancer stagingMRIPetition asks FDA to implement new OEM rule for MRI safetyWomens ImagingIncorrect AI suggestions influence reader performance on mammographyMRIPodcast: MRI tragedy, key aspects of the Colombini family's lawsuit
![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&fit=crop&h=167&q=70&w=250)
![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)
