John SolePACS/VNAEffective communication is crucial for radiology department successA culture of solid interpersonal communication can serve as a positive contributor to a radiology department's bottom line in many ways. John Sole, the PACS administrator supervisor at Swedish American Hospital in Illinois, explains why improving communication in your department can be beneficial for both your staff and patients.March 19, 2008PACS/VNADeveloping a cost-effective image QA workflow with PACSIn a radiology department, image quality is or should be a primary metric in the department's continuous quality improvement (CQI) arsenal. With PACS becoming an ever-increasing method of viewing studies, institutions need to develop an optimal quality assurance (QA) workflow to fit the new digital viewing paradigm. PACS administrator supervisor John Sole from Swedish American Hospital in Rockford, IL, offers some advice to help your department develop a cost-effective QA workflow.November 20, 2007Page 1 of 1Top StoriesEconomics2027 MPFS proposed rule would lift radiology reimbursementIf finalized, the proposed rule is expected to 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)
