Dermatology Image Annotation FHIR Implementation Guide
0.1.0 - ci-build

Dermatology Image Annotation FHIR Implementation Guide - Local Development build (v0.1.0) built by the FHIR (HL7® FHIR® Standard) Build Tools. See the Directory of published versions

## Examples

This page walks through each example resource in the IG, explaining the clinical scenario and the key elements demonstrated. All examples use synthetic patient data.

#### ExampleMelanomaAnnotation

Scenario: Dr. Robert Johnson, a board-certified dermatologist, reviews a clinical photograph of a lesion on the right upper arm of patient Jane Smith. He performs a full expert annotation, classifying the lesion as malignant melanoma with high confidence. The annotation passes QA review and achieves strong inter-annotator agreement.

Key elements:

- `method` is set to `expert-annotation`, indicating a specialist performed the full annotation without AI assistance
- `component[skinFinding]` uses SNOMED code `372244006 | Malignant melanoma |`
- `component[confidenceScore]` records 0.92, the annotator's self-reported confidence
- `component[qaReviewStatus]` is `approved`, indicating the annotation passed quality review
- `component[interAnnotatorAgreement]` records a kappa of 0.87, reflecting strong agreement
- `derivedFrom` references the source clinical photograph (DocumentReference)

See ExampleMelanomaAnnotation.

#### ExampleAIPrediction

Scenario: Before Dr. Johnson reviews the image, the DermAI Classifier v2.1 analyzes it and produces a preliminary prediction of malignant melanoma with 89% confidence.

Key elements:

- `meta.security` includes the `AIAST` code, marking this as AI-generated content
- `status` is `preliminary` because no clinician has reviewed the prediction
- `method` is fixed to `ai-inference`
- `device` references the AI model Device resource (DermAI Classifier v2.1)
- `component[predictionConfidence]` records 0.89
- `component[inferenceTimeMs]` records 245 ms of model inference time

See ExampleAIPrediction.

#### ExampleAIPredictionProvenance

Scenario: A Provenance resource documents the audit trail for the AI prediction, recording which system created it and from what source data.

Key elements:

- `target` references the ExampleAIPrediction Observation
- `agent.who` references the AI model Device, identifying the system that produced the prediction
- `entity.what` references the source clinical photograph with role `source`

See ExampleAIPredictionProvenance.

#### ExampleAIAssistedConfirmed

Scenario: Dr. Johnson reviews the AI prediction and agrees with the malignant melanoma classification. He confirms the AI result without modification using the quick classify workflow, producing a final annotation.

Key elements:

- `method` is `ai-assisted-confirmed`, indicating the clinician accepted the AI prediction
- `meta.security` retains the `AIAST` code because the result originated from AI analysis
- `status` is `final` because a clinician has reviewed and accepted the finding
- `code` is `quick-classify`, representing the rapid clinician-driven classification workflow
- `performer` references the confirming dermatologist

See ExampleAIAssistedConfirmed.

#### ExampleAnnotationSeries

Scenario: The system groups the expert annotation and the AI-assisted confirmed annotation into a longitudinal series for the same lesion. Change detection determines that the classification remained stable.

Key elements:

- `hasMember` references both ExampleMelanomaAnnotation and ExampleAIAssistedConfirmed
- `component[changeDetected]` is `false`, indicating no change in classification
- `component[changeType]` is `unchanged`
- `component[earliestDate]` and `component[latestDate]` define the temporal range

See ExampleAnnotationSeries.

#### Supporting resources

The examples reference shared Patient, Practitioner, Device, and DocumentReference resources:

- example-patient — Jane Smith, the synthetic patient
- example-dermatologist — Dr. Robert Johnson, the annotating clinician
- example-derm-ai-model — DermAI Classifier v2.1, the AI device
- example-clinical-photo — a DocumentReference for the source clinical photograph (JPEG image of a right upper arm lesion)