The American College of Radiology (ACR) Data Science Institute (DSI) has partnered with the U.S. National Cancer Institute (NCI)-funded Cancer Imaging Archive in an effort to aid developers of artificial intelligence (AI) algorithms in radiology.
The ACR DSI has linked its Define-AI use cases with datasets from the Cancer Imaging Archive (TCIA). Specifically, these datasets have been matched to ACR DSI cancer and noncancer use cases based upon attributes such as body area, modality, and presence of secondary comorbidities.
As a result, developers can create radiology AI algorithms that include defined data elements and DICOM images useful for algorithm inputs, outputs, training, and testing, according to the organization.
![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)
