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

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This page is draft.
 
This page is draft.
  
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== Public Comment and Navigating the AI Interoperability in Imaging Document ==
  
{{#ev:youtube|dMH0bHeiRNg}}
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=== [https://www.youtube.com/watch?v=Sj6qB8VFBy8 Watch the Introductory Video on YouTube!] ===
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The AI Interoperability in Imaging White Paper is a long document. Reviewers are encouraged to provide feedback, even if only on portions of the document. To optimize their efforts, reviewers may consider:
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* Paying particular attention to the Questions in sectionSection 1.4.
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* Focusing on specific areas that align with their interests and expertise:
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** 2.1 Applications of AI in Imaging focuses on activities that AI can be applied to. The use cases in chapter 3 will be driven by these applications. This section may appeal to clinical reviewers.
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*** Note: this white paper specifically strives to broaden the map of applications beyond radiology image analysis. Reviewers are encouraged to consider:
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** Imaging specialties beyond radiology, such as cardiology, pathology, dentistry, ophthalmology, dermatology, etc.
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*** Tasks beyond image analysis, such as ordering, acquisition, reporting, and departmental operations analysis.
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** 2.2 Personas and Systems provides brief definitions that may clarify the subsequent use case sections. This section may appeal to both clinical and technical reviewers.
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** 3 Use Cases focuses on the steps that create, update, and use AI Applications. At the beginning of each Use Case Group and Use Case includes a statement of the task covered in the block. This section may appeal to data scientists and technical reviewers.
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** 4 Entities focuses on common data structures and metadata in AI workflows. This section may appeal to technical reviewers.

Revision as of 23:13, 9 March 2021

This page is draft.

Public Comment and Navigating the AI Interoperability in Imaging Document

Watch the Introductory Video on YouTube!

The AI Interoperability in Imaging White Paper is a long document. Reviewers are encouraged to provide feedback, even if only on portions of the document. To optimize their efforts, reviewers may consider:

  • Paying particular attention to the Questions in sectionSection 1.4.
  • Focusing on specific areas that align with their interests and expertise:
    • 2.1 Applications of AI in Imaging focuses on activities that AI can be applied to. The use cases in chapter 3 will be driven by these applications. This section may appeal to clinical reviewers.
      • Note: this white paper specifically strives to broaden the map of applications beyond radiology image analysis. Reviewers are encouraged to consider:
    • Imaging specialties beyond radiology, such as cardiology, pathology, dentistry, ophthalmology, dermatology, etc.
      • Tasks beyond image analysis, such as ordering, acquisition, reporting, and departmental operations analysis.
    • 2.2 Personas and Systems provides brief definitions that may clarify the subsequent use case sections. This section may appeal to both clinical and technical reviewers.
    • 3 Use Cases focuses on the steps that create, update, and use AI Applications. At the beginning of each Use Case Group and Use Case includes a statement of the task covered in the block. This section may appeal to data scientists and technical reviewers.
    • 4 Entities focuses on common data structures and metadata in AI workflows. This section may appeal to technical reviewers.