A resource that captures the input and output data used by an AI model in generating or enhancing FHIR resources.

Given that this is a profile on DomainResource, it can't be used in an IG, but it can be used for validation purposes.

A Device that represents an AI system, such as a Large Language Model (LLM) or other AI model. This profile includes a recommended set of codes for Device.type to indicate the type of AI system. The extension AIKind is used to specify the more specific kind(s) of AI technology and techniques employed by the AI system. The extension ModelCardDescription is used to reference a Model-Card that is always used with the AI system.

A DocumentReference that contains an input prompt for an AI system. The Input Prompt is the information that is input into the AI system to produce an output. This DocumentReference can be used to capture the Input Prompt that was used with a given AI system, and can be referenced from the Provenance that tracks the usage of the AI system. The AI Prompt is typically encoded in markdown, but does not need to be. This DocumentReference would be referenced in the Provenance as the input to an Activity that represents the usage of the AI system, and the AIDevice would be referenced as the agent in that Provenance. This allows for traceability from the output back to the Input Prompt and the AI system that produced it.

A DocumentReference that contains a Model-Card describing an AI system.

An AI Provenance is a record of the use of an AI model in generating or enhancing FHIR resources. It captures the details of the AI model used, the input data given to the AI, and the output generated by the AI.

When the Device is described by a Model-Card, this extension can be used to reference the Model-Card.

Expresses the more specific kind(s) of AI technology and techniques.

Subset from HL7, plus those defined here

Subset from HL7, plus those defined here. For use in .meta.security to indicate AI was used in some way.

This CodeSystem contains codes for the provenance indications used in .meta.security and elsewhere that indicate that the AI system has been involved.

This CodeSystem contains codes for the DocumentReference.type and DocumentReference.category that indicate that the DocumentReference is an Input-Prompt or one of a set of Model-Card(s).

This CodeSystem contains codes for the Device.type that indicate that the Device is an AI. The codes here were created by AI.

A Procedure resource that is created by an AI system and verified by a human.

Example of the CHAI example pdf using Binary.

Example of the CHAI example xml using Binary.

A lab result Observation resource that is created by an AI system and verified by a human.

A Bundle resource containing AI generated resources

A Device that has a Model-Card. Given that it is understood that ModelCards are Markdown, this could simply go into the .note.

A Device that has an attached Model-Card.

This DiagnosticeReport is derived from FHIR Core R4 DiagnosticReport id f202. This use contains a DiagnosticReport with inline Artificial Intelligence asserted security labels for the conclusion and conclusionCode, as well as a ServiceRequest.

• The DiagnosticReport is tagged with the security label PROCESSINLINELABEL, to indicate that there is inline security labeling.
• The DiagnosticReport.conclusion and DiagnosticReport.conclusionCode elements are tagged with the Artificial Intelligence asserted security label AIAST.

An example of a Model-Card DocumentReference that contains the Model-Card in CHAI format. Where the Model-Card is attached.

Note: This is not functioning at this time due to problems with IG Publisher and attachments using the ig-loader where the contentType is specified. When it is specified the ig-loader will not work, but we need it to be specified to meet our profile and slicing requirements.

An example of a Model-Card DocumentReference that contains the Model-Card in CHAI format. Where the Model-Card is attached using Binary resources.

An example of a Model-Card DocumentReference that contains the Model-Card in CHAI format. Where the Model-Card is referenced from the CHAI GitHub repository.

An example of a Model-Card DocumentReference that contains the Model-Card in YAML and Markdown formats.Using an example from HuggingFace.

This observation is derived from FHIR Core R4 Observation id glasgow. This use contains a Glasgow Coma Scale observation with Artificial Intelligence asserted security labels for the whole Resource.

Note that the example I took, I assumed was a good one for AIAST. But I am not sure that it is, especially since the outcome is not well coded, using ucum score values.

Note that there is no .performer as that element can't hold a Device, and we are modeling this as being wholly authored by the AI. Thus I use the extension alternate-reference.

A DocumentReference that contains the input prompt provided to the AI system. – Lorem ipsum text is used as a placeholder.

Generate a lorem ipsum text to serve as placeholder copy for use in design, development, and publishing. 

1. Specify the exact amount of text or the number of paragraphs required (e.g., 1 paragraph, 3 paragraphs, etc.). 
2. Create the lorem ipsum text using a classic style or introduce slight variations while keeping the nonsensical nature to suit the requested length. 

Ensure that the text maintains a good balance between readability and the traditional lorem ipsum style, giving a realistic impression of how the text will impact the overall layout and design.

# Output Format
- Provide a continuous block of lorem ipsum text corresponding to the specified amount needed.

A DocumentReference that contains the input prompt provided to the AI system. – to generate a Patient resource.

System Prompt

You are a healthcare data specialist that converts natural language patient information into valid FHIR Patient resources. Your task is to extract relevant patient demographics and create a well-formed FHIR Patient resource that is fully conformant with FHIR US Core 6.1.0 specifications.

Requirements:

- Generate valid JSON that conforms to FHIR R4 Patient resource structure
- Ensure compliance with US Core Patient Profile (US Core 6.1.0)
- Include all required US Core elements when data is available
- Use appropriate FHIR data types and value sets
- Generate a unique resource ID using UUID format
- Apply proper FHIR coding systems and terminologies
- Handle missing data appropriately (omit optional fields when data unavailable)
- Use standard US address formatting
- Apply proper date formatting (YYYY-MM-DD)
- Include appropriate extensions when necessary for US Core compliance

US Core 6.1.0 Patient Profile Requirements:

- Must include: identifier, name, gender, birthDate
- Should include: address, telecom, race, ethnicity when available
- Use US Core extensions for race and ethnicity
- Follow US postal address conventions
- Use appropriate terminologies (e.g., HL7 AdministrativeGender, OMB race categories)

Data Mapping Guidelines:

- Extract patient name and structure as HumanName with family/given components
- Map gender terms to FHIR AdministrativeGender codes (male, female, other, unknown)
- Convert birth dates to FHIR date format
- Structure addresses using Address data type with appropriate use codes
- Map race/ethnicity information using US Core extensions with appropriate OMB codes
- Generate medical record number as primary identifier when not provided
- Include meta.profile reference to US Core Patient profile

Output Format:

- Provide only the valid FHIR JSON resource without additional commentary or explanation.

User Prompt

- Convert the following patient information into a FHIR Patient resource conformant with US Core 6.1.0:

`Jane Doe is a white female born on November 15, 1950. She lives at 123 Main Street, Anytown, Michigan, zipcode 12345.`

A DocumentReference resource that represents a PDF document containing lab results for a patient. This is provided to an AI, which interprets and creates FHIR Resources.

In the attached example the patient's name is Alton Walsh and the lab test is an HbA1C. All the FHIR resources in the bundle have been created by the AI, so they should be tagged accordingly.

An example of an Oxygen Saturation Observation, where the device used is an AI system.

An example Patient resource generated by AI

A Provenance resource that captures the full AI process, including the Model-Card, input, and output.

This is a full example of how to capture the AI process in FHIR.

• One output that this Provenance resource is documenting:
  • an Patient resource
• Two agents
  • a verifier (human) who verifies the AI output
  • an author (AI system) who generated the output
• One entity that is the AI Input Prompt
  • Where the Input Prompt is a DocumentReference resource that contains the input prompt provided to the AI system.
  • Where the Input Prompt is a contained resource in the Provenance resource.
  • Where the Input Prompt is associated with the clinician which provided it

A Provenance resource that captures the full AI process, including the Model-Card, input, and output.

This is a full example of how to capture the AI process in FHIR.

• Two outputs that this Provenance resource is documenting:
  • an Observation resource (e.g., lab result)
    • with Observation.interpretation being attributed to this Provenance
  • a CarePlan resource (e.g., follow-up care plan)
• Two agents
  • a verifier (human) who verifies the AI output
  • an author (AI system) who generated the output
• Two entities that were clinical resources provided to the AI system
  • a DocumentReference resource (e.g., patient summary)
  • an Observation resource (e.g., lab result)
• One entity that is a PlanDefinition resource (e.g., care plan definition)
• One entity that is the AI Input Prompt
  • Where the Input Prompt is a DocumentReference resource that contains the input prompt provided to the AI system.
  • Where the Input Prompt is a contained resource in the Provenance resource.
  • Where the Input Prompt is associated with the clinician which provided it

A Provenance resource that documents the addition of followUp text in a Procedure by an AI system. No Model-Card was used.

A Provenance resource that documents the creation of a Lab result Observation resource by an AI (device), verified by a human.

• The AI system is represented as a Device resource.
• No Model-Card was used.
• The Input is a PDF Lab result.

A Provenance resource that documents the creation of a Lab result Observation resource by an AI (device), verified by a human.

• The AI system is represented as a Device resource.
• No Model-Card was used.

An AI system that authored a resource.