FHIR-native dermatology image annotation. An open standard for structured annotation workflows that integrate with any EHR.
View the FHIR Implementation GuideThree profiles built on FHIR R4 Observation, compatible with any FHIR-enabled EHR. Structured annotation results that clinicians and researchers can query, aggregate, and exchange.
Aligned with the HL7 AI Transparency on FHIR framework. Every AI prediction carries provenance metadata, model identification, and confidence scores as first-class FHIR elements.
Designed for SMART on FHIR launch workflows. Annotation results flow back to the patient record as standard FHIR Observations, queryable via the FHIR REST API.
Published under Creative Commons. The first FHIR IG for dermatology image annotation, built for community adoption and feedback through the HL7 standards process.
Core profile for structured annotation results. Captures skin findings, confidence scores, segmentation masks, and bounding boxes as FHIR Observation components.
AI model predictions with mandatory transparency metadata. Always marked as preliminary, linked to a Device resource identifying the model, with inference timing and heatmap data.
Temporal lesion tracking across multiple time points. Groups DermatologyImageAnnotation observations chronologically with change detection and classification.
A companion white paper describes the clinical and technical rationale behind the Annotate MD standard, including workflow analysis, interoperability considerations, and implementation guidance.
Coming soon
We're building the platform behind this standard. Join the waitlist to get updates on the annotation tool, EHR integration, and pilot programs.