AI Results specifies how medical imaging analysis results are reliably stored, retrieved, and displayed. The central use case involves results generated by artificial intelligence (AI Model) algorithms.
This profile establishes baseline data handling and presentation capabilities for an image display product to be “AI-Ready”. Result generation products can leverage these data formats to be compatible with a variety of displays and site workflows.
- content formats for encoding data
- transactions for moving that content around
- behaviors for basic handling and display of the content
- Interoperable Results:
- Results need to be presented to radiologists in their reading environment. This profile provides interoperability between result generation products and radiology reading workstation systems/software.
- Study Integrated Results:
- Radiologists expect AI-generated results to be presented in the context of the study to which they apply and expect them to supplement (rather than replace) traditional image analysis results and thus a given study will be composed of acquired images, AI results, and traditional clinical data. The result objects defined in this profile are defined for the existing imaging data storage infrastructure.
- Effective Presentation:
- Effective use of results hinges on presenting them in conjunction with the associated images during the busy process of reading the study.
- Convergence of Result Encoding:
- Many AI results are results that a human could otherwise have produced, and those human results may be used as training data for the AI. Analysis results might also be used by other AIs, e.g., in a GAN (generative adversarial network). AI and non-AI results need to be handled together. Convergent encoding of results facilitates this, as well as data pooling and sharing between sites.
- Display Primitives:
- It is unrealistic to expect radiology displays to implement specific display capabilities for each of the myriad of algorithms being developed. To minimize implementation complexity for displays, and avoid needing different software for each new analysis result, compose analysis results from a reasonable set of primitives.
<A few paragraphs, if appropriate, providing more details (mostly in user-speak, not tech-speak) on what the profile does and how it works.>
<If the user might be familiar with the mechanisms used by the profile, you can mention them here. E.g. Evidence Documents is based on DICOM Structured Report (SR) Templates.>
<If the user might have an appreciation for the problems addressed in the profile, you can mention them here, but keep it short. E.g. Mapping HL7 Order fields to DICOM Modality Worklist attributes can be inconsistent in the marketplace, so Scheduled Workflow provides vendors with more detailed instructions.>
- PACS systems may store, manage, and/or display Evidence Documents.
- Display systems may query, retrieve and display Evidence Documents.
- Reporting workstations may retrieve, process and include details from Evidence Documents in reports
Actors & Transactions:
<Insert an actor-transaction diagram, and or list of Content Definitions>
Profile Status: Trial Implementation
Documents: AI Results Profile - TI
<List profiles this one depends on, profiles that depend on this one, profiles that are synergistic with this one. Start with the name of the other profile as a link and then explain the relationship.>
- Reporting Workflow [RWF] may use Evidence Documents as inputs to the reporting process.
- Simple Image & Numeric Reports [SINR] may include data copied from Evidence Documents.
- Cross-enterprise Document Sharing for Imaging [XDS-I] can be used to share Evidence Documents between sites over a network.
- Portable Data for Imaging [PDI] can store Evidence Documents on media such as CDs.
- Import Reconciliation Workflow [IRWF] can fix patient ids, etc. of Evidence Documents when importing.
This page is based on the Profile Overview Template