ACR DSI Releases 2 New Non-Interpretive Reading Room Use Cases

The ACR Data Science Institute® (ACR DSI) released two new non-interpretive reading room use cases in May, adding to the guidelines DSI provides to the artificial intelligence (AI) community. Developers can freely access these two new use cases and five additional reading room cases on the DSI website

The new non-interpretive reading room use cases include: 

  1. Hands Free Navigation for Abdominal Imaging: As the volume of studies and number of images per day increases for radiologists, navigating through the large volume of images has become a rote physical task which can lead to repetitive stress injury. Having hands-free navigation using voice assistant software with automated search patterns tailored to each radiologist would reduce these injuries, facilitate the workflow of radiologists with disabilities, free up hands/attention for other tasks (such as teaching), standardize individual search patterns/checklists (potentially decreasing missed findings due to interruptions) and improve job satisfaction overall.
  2. Cyst Identification/Characterization: Accurately characterizing a cystic lesion on multidetector computed tomography (CT) examinations is often time-consuming, particularly when comparing the same lesion on multiple prior examinations or even on different imaging modalities. Implementing a PACS-centered algorithm that can measure and characterize a lesion and generate a text output to be imported into the radiology report can increase the efficiency of lesion characterization. Additionally, inserting standardized descriptive text into structured radiology reports will enhance reporting consistency and may also decrease inter-observer variability.   

Register here for a free upcoming Non-Interpretive AI webinar, “Beyond Interpretation: Unleashing the Potential of Non-Interpretive AI in Radiology,” on Wednesday, May 31, from 7–8 pm ET. The key learnings from the webinar and Q&A panel will include understanding the breadth of non-interpretive AI tools available for clinical practice, how non-interpretive AI tools may impact the radiologic practice from an operational and clinical standpoint, and the outlook for emerging non-interpretive AI tools in the marketplace. 

About ACR DSI use cases: ACR DSI use cases are scenarios where the use of AI may help improve clinical settings and medical imaging care. They provide structured data elements for training, testing and monitoring algorithms to help those developing AI to create new AI models, or improve existing AI products. ACR DSI has released more than 200 use cases developed by subspecialty data science panels of experts