Royal Philips has launched a new version of its IntelliSpace Radiology Analytics business intelligence software at this week's Society for Imaging Informatics in Medicine (SIIM) meeting in Pittsburgh.
Radiology Analytics 1.2 includes a number of improvements designed to make the application more useful for analyzing the operations of a radiology facility, according to the company. It also aggregates data from multiple sources, including DICOM, HL7, and PACS.
Radiology departments and imaging centers are being required to monitor and track their performance as part of federal efforts to move the U.S. healthcare system from volume to value. Radiology Analytics 1.2 is designed to help them meet these requirements, according to Philips.
For example, users of the software can configure the application to track the key performance indicators (KPIs) they are most interested in, including radiologist efficiency, modality utilization, technologist efficiency, and referral patterns. By integrating the software with Philips' IntelliSpace PACS network, Philips customers can begin tracking their performance without the hassle of a major software integration.
Data in IntelliSpace Radiology Analytics can also be paired with a front-end assessment through Philips PerformanceBridge Practice service, enabling users to identify opportunities and gaps within a health system. Following an assessment, Philips can act as a partner in ongoing continuous improvement, providing detailed benchmarking and insights to help achieve productivity gains.
Philips is offering Radiology Analytics 1.2 to its installed base of IntelliSpace PACS users. The company will begin rolling out the software to customers on an evaluation basis in the third quarter, with commercial shipments beginning sometime thereafter.
![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.auntminnieeurope.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)
