Demos
Why Does the Gap Between Extracted Entities and Standard Medical Codes Matter?
Healthcare runs on codes: ICD-10 for diagnoses, LOINC for lab observations, CPT for procedures, SNOMED CT for clinical concepts, RxNorm for medications, MeSH for biomedical literature. EHRs, billing systems, research databases and regulatory submissions exchange clinical data through these codes.
When NLP extracts entities from clinical text, what it captures are surface forms, the words as written in the note: “chest pain,” “troponin I,” “left knee arthroscopy.” Coded data exchange needs each of these strings mapped to a concept in a standard terminology. Closing that gap has traditionally meant one of two workarounds: a post-processing resolution step disconnected from the human review workflow, or manual coding by annotators who look up codes themselves, which introduces inconsistency and slows throughput. Neither keeps the coded result connected to the annotation decision that produced it, and neither enables a human-in-the-loop workflow where a reviewer verifies the entity label and the resolved code in one pass.
Example: an annotator reviewing a synthetic oncology progress note sees the entity “stage IIIA non-small cell lung cancer” and needs an ICD-10-CM code; “carboplatin AUC 5” needs an RxNorm code; “ECOG performance status 1” needs a mapped SNOMED CT concept. Under the old workaround, that’s three separate lookups outside the annotation tool, each one a place for the coded result to drift from the entity it describes.
How Does Generative AI Lab Integrate Medical Terminologies into Annotation Projects?
Medical Terminologies are available as native resources in the Generative AI Lab Hub, alongside NLP models and other clinical resources. Teams can browse available terminologies, review their coverage, and download the resources required for their annotation projects. Once downloaded, terminologies become shared assets that can be used across multiple projects without redundant setup.
Terminology access follows the same licensing model as Healthcare NLP pipelines. Downloading requires a valid license with the appropriate healthcare scope.
Generative AI Lab makes downloaded vocabularies available within the application. No additional deployment, external artifacts, or coding is required to start using them in annotation projects.
How Are Medical Terminologies Linked to Annotation Labels?
The integration between medical terminologies and annotation projects is configured through the Label Configuration interface. When creating or editing a project, teams can enable Medical Terminologies in the Customize Label interface and associate specific terminologies with annotation labels. For example, a clinical annotation project might associate ICD-10 with diagnosis labels, LOINC with laboratory observation labels, RxNorm with medication labels, and CPT with procedure labels. These associations determine which terminology is used to resolve entities extracted under each label. One configuration restriction is that Medical Terminologies cannot be combined with legacy AI resolver models in the same configuration. Generative AI Lab validates the configuration and identifies conflicts when both are selected.How Does Automatic Medical Code Resolution Work During Pre-Annotation?
Generative AI Lab supports automatic medical code resolution as part of the pre-annotation workflow. When a pre-annotation pipeline extracts an entity associated with a label linked to a medical terminology, the corresponding terminology is used to identify a proposed standard code.
For example, consider a clinical note documenting type 2 diabetes mellitus without complications.
During pre-annotation, the NLP pipeline extracts the diagnosis and associates it with the configured Diagnosis label. The linked ICD-10-CM terminology can then resolve the diagnosis to code E11.9.
The annotator sees E11.9 next to the Diagnosis label on the extracted entity. This allows reviewers to verify or correct both the extracted entity and its terminology mapping without performing a separate lookup.
The entity, its annotation label, and the associated code remain connected throughout the annotation workflow and are included in the exported annotation data.
How Can Annotators Manually Resolve Medical Codes During Human Review?
Automatic terminology resolution does not eliminate the need for human judgment. Clinical terminology mapping can involve ambiguous expressions, multiple potentially valid codes, or incomplete information in the source document. In these situations, annotators need to examine the clinical context before selecting the appropriate terminology concept. Generative AI Lab provides manual terminology lookup within the annotation interface.
When an automatically suggested code requires correction, or when no suitable code has been assigned, annotators can search the terminology associated with the relevant label and select the appropriate concept without leaving the application.
Keeping manual lookup within the annotation interface allows reviewers to examine the source text, annotation label, and terminology candidates together.
The selected code remains attached to the annotation, preserving the relationship between the original clinical expression and its standardized representation.
This integrated human-in-the-loop process helps maintain consistent annotation decisions while retaining the clinical judgment required for complex terminology mapping.
