News & Events

An article in the April issue of the Journal of the American College of Radiology (JACR®) documents how AI-LAB™ was successfully used to test an external COVID-19 Chest Radiograph (CXR) Artificial Intelligence (AI) algorithm on local data by a clinical radiology department with no dedicated data science infrastructure. The experiment shows generalizability of the Pulmonary X-ray Severity (PXS) score model from one practice to another. More importantly, the authors explain how a modest investment of resources and effort can empower a real-world clinical department to participate in data science. This successful test demonstrates AI-LAB’s value for local testing and model validation

The concepts covered in the MSK videos are a starting point for software development teams to understand the clinical relevance of ACR DSI AI use cases. All videos in the series reference specific MSK use cases, include standard DICOM (Digital Imaging and Communications in Medicine) images and labels; a breakdown of how a proposed algorithm might solve an essential problem; and explain why these highly solvable problems offer immediate value.

A recent SIIMcast podcast focusing on the American College of Radiology’s ® (ACR) Data Science Institute® (DSI) features data scientists Laura Brink and Kendall Schmidt, PhD and Chris Treml, Director of DSI.

Radiologists can now register their practices to take part in the next-generation American College of Radiology Data Science Institute® artificial intelligence (AI) federated learning program hosted on the AI-LAB™ platform.

The final white paper on AI Interoperability in Imaging was released by the Integrating the Healthcare Enterprise (IHE) Radiology Technical Committee on October 12. The paper sets the landscape for artificial intelligence (AI) interoperability and function and provides “recipes” for standards based workflow.

The American College of Radiology® Data Science Institute® (ACR DSI) and the American Academy of Ophthalmology have announced a collaboration that will expand ACR DSI’s groundbreaking AI-LAB™ platform to include ophthalmic imaging. This initial collaboration building upon the successful AI-LAB platform is a natural expansion because modern ophthalmology practices produce the large quantity of images needed for artificial intelligence training. More information about the collaboration is in the joint ACR/AAO press release.

The American College of Radiology® (ACR®) Data Science Institute® (DSI) and the Cancer Imaging Archive (TCIA), funded by the National Cancer Institute (NCI) at the National Institutes of Health (NIH), have teamed to connect use cases and datasets to speed medical imaging artificial intelligence (AI) development. TCIA datasets have been matched to ACR DSI cancer and non-cancer use cases based upon attributes such as body area, modality, and presence of secondary comorbidities. TCIA data are available under Creative Commons Attribution Licenses, and most are freely available for commercial use for machine learning purposes.

A new white paper on AI Interoperability in Imaging has been released for public comment by the Integrating the Healthcare Enterprise (IHE) Radiology Technical Committee. A joint effort between medical imaging societies, industry and the radiology community, the paper sets the landscape for artificial intelligence (AI) interoperability and function. It provides a comprehensive map of the needs, problems and challenges that must be addressed to achieve an ecosystem of interoperable products that support all the processes and tasks that make up AI in Imaging. An IHE video introduces the white paper’s content.

The ACR Data Science Institute® (ACR DSI) released six new neuroradiology use cases in February, adding substantially to the scenarios DSI provides to the artificial intelligence (AI) community. Developers can freely access 21 neuroradiology use cases on the DSI website.

The expansion of artificial intelligence (AI) into the healthcare arena and radiology is accelerating rapidly. Standards bodies such as IHE (Integrating the Healthcare Enterprise), DICOM (Digital Imaging and Communications in Medicine), and HL7/FHIR (Fast Healthcare Interoperability Resources) continue to actively define the inclusion of new technologies to facilitate the integration of AI into healthcare.

The American College of Radiology Data Science Institute® (ACR® DSI) now offers a catalog of 111 FDA-cleared medical imaging artificial intelligence (AI) algorithms (class II) searchable by company, subspecialty, body area, modality and date cleared. Carefully designed for radiologists, the catalog dramatically reduces the time required to accomplish the formerly complex task of sorting through the algorithms available to those in medical imaging.

Applications for the 2021 Informatics Fellowship will be accepted from February 1 - March 31, 2021.

The expansion of artificial intelligence (AI) into the healthcare arena and radiology is accelerating rapidly. Standards bodies such as the Integrating the Healthcare Enterprise (IHE), Digital Imaging and Communications in Medicine (DICOM) and Fast Healthcare Interoperability Resources (HL7/FHIR) continue to actively define the inclusion of new technologies to facilitate the integration of AI into healthcare. The American College of Radiology® (ACR®) continues to be an active participant in the development and maintenance of these standards.

The IHE Radiology Technical Committee has published AI Workflow for Imaging (AIW-I) Rev. 1.0. The document can be downloaded here. Comments submitted by April 29, 2020 will be considered by IHE Radiology. Comments can be submitted here. The AI Workflow for Imaging Profile addresses workflow use cases involving the request, management, and performance of inference tasks on digital image data acquired by an imaging modality.

DICOM Supplement 219 has begun a 12 month Trial Implementation period before being voted on by the DICOM Standards Committee. This supplement will make report data easier to consume for AI/ML algorithm developers and allow for interactive annotation exchange in a world where AI and PACS will be best of breed and not necessarily all the same vendor.

The IHE Radiology Technical Committee has published AI Results (AIR) Rev 1.0 for public comment. The document can be downloaded here . Comments submitted by April 9th, 2020 will be considered by IHE Radiology. Comments can be submitted here . This AI Results Profile addresses the capture, distribution, and display of medical image analysis results. The central use case involves results generated by artificial intelligence (AI) algorithms.

The American College of Radiology® (ACR®) Data Science Institute (DSI) recently released a new list of FDA-cleared artificial intelligence (AI) algorithms related to medical imaging. Each model includes a summary with the model manufacturer, FDA product code, body area, modality, predicate device, product testing and evaluation related to product performance and clinical validation.

The American College of Radiology (ACR) Data Science Institute® (DSI) released 90 clinical and non-interpretive use cases for the radiology community in November to more than double the number of freely available use cases on the DSI website to 140.

ACR AI-LAB™ offers radiologists tools designed to help them learn the basics of AI and participate directly in the creation, validation and use of healthcare AI.

A first-of-its-kind series of standardized artificial intelligence (AI) use cases from the American College of Radiology Data Science Institute™ (DSI ) will accelerate medical imaging AI adoption.