Skip to Main Content

About Use Cases

Learn more about our use cases and how to interpret them.

Use Cases as Corner Stones

ACR® DSI use cases describe specific clinical scenarios in which an AI application adds value and suggests features to garner trust and endorsement from the community. Use cases define basic requirements to complete certain automation and nest within broader IT environments. In addition to guidance and standards for developers, this list indicates the applications the DSI supports with curated datasets codified according to our use case specifications.

Building wide-scale healthcare AI requires access to robust structured data. The DSI is part of a global initiative to open access to data for AI development and is engaging institutions across the country on data access projects. This project relies on use cases, and each use case describes the conditions that algorithms are expected to reliably execute. Once in the field, the DSI leverages the ACR registry infrastructure connected to facilities across the country to provide analytics on algorithm performance, which can be used as real-world evidence or a basis for updates. All of these initiatives begin with scrupulously describing relevant algorithmic functions and clinical insights which ensure the application is effectively utilized.

View Our Use Cases

Use case interpretation

View use cases

ACR DSI use cases are organized into

five sections

A Quick Understanding

The overview section contains metadata on the use case which provides users with a quick understanding of use case content. Related use cases may be bundled under a similar project represented in a tagging system.

View use cases

The Ideal Algorithm

This section describes the ideal algorithm that could improve the radiology care stream. Content is subdivided into the Value Proposition, Narrative and Workflow Description. The sections explain how certain AI automation is valuable to radiology workflows and the precise clinical scenario. Suggestions on how an application would integrate with common radiology IT infrastructure are included.

View use cases

Earn Trust in Clinical Environments

This section pinpoints the variants that may affect the presentation of the image and are often evaluated when determining a finding or recommending clinical management. This content serves to recommend the types of data that algorithms are expected to handle to earn trust in clinical environments.

The variants listed here are not meant to be comprehensive but are instead variants that may not be obvious to those without clinical backgrounds. We recommend ensuring that algorithms can handle these imaging scenarios.

View use cases

Standard Terminology for Executing an Algorithm

This section specifies the standard terminology and markup for executing an algorithm. The source of these standards is RadElements, a collaborative work between the ACR, RSNA and major standards-bodies. Vendors pursuing validation and clinical implementation should expect to handle and transmit these data elements when performing the use case.

Outputs are organized by primary and secondary elements. This is intended to provide developers ideas on how to unfold additional features on their product to improve clinical decision making.

View use cases

Additional Opportunities

This section highlights additional opportunities for developers to enhance their product. It may contain user interface suggestions or more abstract ideas on the evolution of the application.

View use cases

Frequently AskedQuestions

Get answers to the most commonly asked questions.

Use case is a generic term for “a methodology used in system analysis to identify, clarify and organize system requirements.” Each DSI use case provides narrative descriptions and standards which specify the goal of the algorithm, the required clinical input, how it should integrate into the clinical workflow, and how it could interface with users and tools.

These first-of-their-kind use cases are building a framework to facilitate the development and implementation of artificial intelligence (AI) applications that will help radiology professionals in disease detection, characterization and treatment. They enable data scientists to produce algorithms that:

  • Address relevant clinical questions and can improve patient care
  • Can be implemented across multiple electronic workflow systems
  • Comply with requirements to submit data to relevant registries to enable ongoing assessment
  • Comply with applicable legal, regulatory and ethical requirements

Any developer looking for a high impact problem to solve with AI would be interested in use cases. Despite the AI hype, at this stage most successful companies are focused on key narrow AI problems, and the smaller those problems the better. In radiology AI most developers will want to maintain a sharp focus on a specific medical problem, develop an algorithm that works alongside and augments humans (providing clinical decision support), gain access to good data for algorithm training and validation, and maintain awareness of the federal regulatory process throughout.

Want to contribute to use case development?

Learn how to participate