Difference between revisions of "AI Interoperability in Imaging White Paper"

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(Navigating the Whitepaper: changed whitepaper to white paper)
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The paper’s use of “imaging” as a shorthand is intended to cover all forms of medical imaging, not just radiology.
 
The paper’s use of “imaging” as a shorthand is intended to cover all forms of medical imaging, not just radiology.
  
The IHE Planning and Technical Committees will use this whitepaper to identify logical groups of needs that would constitute functional Profiles. The whitepaper content will help ensure the Profiles are properly scoped and do not overlook key issues.
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The IHE Planning and Technical Committees will use this whitepaper to identify logical groups of needs that would constitute functional Profiles. The white paper content will help ensure the Profiles are properly scoped and do not overlook key issues.
  
 
==Navigating the White Paper==
 
==Navigating the White Paper==

Revision as of 18:40, 11 March 2021

This white paper is intended to document:

  • tasks related to medical imaging that might be performed or supported by AI models
  • "use cases" that describe the steps required to create and use AI Models

The paper’s use of “imaging” as a shorthand is intended to cover all forms of medical imaging, not just radiology.

The IHE Planning and Technical Committees will use this whitepaper to identify logical groups of needs that would constitute functional Profiles. The white paper content will help ensure the Profiles are properly scoped and do not overlook key issues.

Navigating the White Paper

Although the white paper is a long document, readers may use the document structure to focus on sections of interest.

  • Section 2.1 - lists examples of tasks to which AI can be applied. They are collected into 8 "stages" in the imaging pipeline starting with placing orders.
  • Section 2.2 - provides brief definitions of the personas and systems that participate in the subsequent use case sections.
  • Section 3.1 & 3.2 - describe managing repositories and assembling datasets for training, testing and validation
  • Section 3.3 & 3.4 - describe creating and distributing AI models
  • Section 3.5 & 3.6 & 3.7 - describe external validation, clinical use, and feedback for AI models
  • Section 3.8 - describes common mechanics like de-identification, annotation, and provenance
  • Section 4 - starts sketching common data structures and metadata in AI workflows.

Public Comment

Watch the Introductory Video on YouTube! for an overview of the purpose and content of the whitepaper.

Reviewers are encouraged to provide feedback, even if only on portions of the document.

  • consider focusing on specific sections (see above) that align with your interests and expertise
  • review the Questions in Section 1.4. on which the committee is particularly seeking feedback
    • Have imaging specialties beyond radiology, such as cardiology, pathology, dentistry, ophthalmology, dermatology, etc. been adequately covered?
    • Have tasks beyond image analysis, such as ordering, acquisition, reporting, and departmental operations analysis been adequately covered?