# Examples - Dermatology Image Annotation FHIR Implementation Guide v0.1.0

* [**Table of Contents**](toc.md)
* **Examples**

## Examples

### 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](Observation-ExampleMelanomaAnnotation.md).

#### 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](Observation-ExampleAIPrediction.md).

#### 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](Provenance-ExampleAIPredictionProvenance.md).

#### 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](Observation-ExampleAIAssistedConfirmed.md).

#### 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](Observation-ExampleAnnotationSeries.md).

#### 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)

