Difference between revisions of "AI Interoperability in Imaging White Paper"
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This page is draft. | This page is draft. | ||
+ | == Public Comment and Navigating the AI Interoperability in Imaging Document == | ||
− | + | === [https://www.youtube.com/watch?v=Sj6qB8VFBy8 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. |
Revision as of 23:13, 9 March 2021
This page is draft.
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.
- 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.