MEDICAL DATA ANNOTATION & LABELLING
Medical data annotation
by licensed clinicians
Most annotation vendors will tell you their labelers are trained. Ours are practicing clinicians — and for medical data, that difference decides whether your ground truth is worth training on.
Medcase recruits licensed clinicians matched to the exact specialty, sub-specialty, modality and clinical context your dataset requires, not a general physician panel. The radiologist reading your pulmonary embolism cases has thoracic experience and real PE volume. The pathologist annotating your slides works in that disease area. That matching is the product.
20K+
Healthcare professionals
in network
140+
Countries
represented
100+
Specialties
200+ sub-specialties
55+
Languages
supported
THE DIFFERENCE THAT DECIDES IT
Why clinical judgment cannot be crowdsourced
Medical data encodes meaning indirectly. A discharge summary carries chronology, uncertainty and implication that a generalist annotator reads past. An imaging study needs a reader who knows what a subtle finding looks like in the modality and anatomy in front of them. A histopathology label depends on diagnostic criteria, specimen type, stain and study objective all at once.
When the annotator lacks that context, the errors are not random — they are systematic, and they teach your model the wrong thing consistently. Clinician-led annotation closes the gap between the label schema you designed and how medicine is actually documented.
Every task is performed by a licensed, specialty-matched clinician — not a crowdworker.
WHAT WE ANNOTATE
IMAGING ANNOTATION
X-RAY · CT · MRI · PATHOLOGY
Finding identification, segmentation review, classification, quality review and other protocol-defined tasks. The reviewer requirement is scoped before recruitment, so specialty, sub-specialty, modality and case experience are part of the dataset design rather than something discovered late.
↓ What that looks like
Board-certified radiologists, scoped to modality and anatomy · thoracic readers with real PE volume where the dataset
needs them.
WAVEFORM, VIDEO & AUDIO
ECG · EEG · SURGICAL VIDEO · CLINICAL AUDIO
Time-based clinical data needs a reader who knows what normal looks like in that lead, that procedure, that specialty. Tasks include event marking, rhythm and artifact classification, phase and step labeling in procedural and surgical video, device-output review, and transcription review of consultation audio.
↓ What that looks like
An arrhythmia marked on the right beat · a surgical phase boundary placed where the operation actually changes · dictation checked against what was said, not what sounds plausible.
CLINICAL NLP & EHR
NOTES · SUMMARIES · RECORDS
Entity labeling, classification, relation review, summarization evaluation, model-output verification and quality assurance. The protocol comes first: label boundaries, edge cases, hierarchy rules, how uncertainty is handled, and when a reviewer should escalate rather than guess.
↓ What that looks like
An oncology note, a cardiology report and an ED encounter may need different clinical backgrounds even when the annotation format is identical.
MATCHING IN PRACTICE
Not "radiologists" — the exact radiologist.
EXAMPLE - IMAGING
Project requirements:
Annotation for a pulmonary-embolism detection model, US CT data.
↓ Medcase match
US board-certified thoracic radiologist · fellowship trained · substantial weekly PE volume · active hospital caseload.
EXAMPLE - PATHOLOGY
Project requirements:
A defensible reference standard, not a single-reader opinion.
↓ Medcase match
Alignment testing on a calibration set · three-pathologist review · defined disagreement and adjudication rules.
EXAMPLE - REGULATED
Project requirements:
MRI segmentation datasets supporting FDA-related work.
↓ Medcase match
Board-certified radiologists, reviewed against the sponsor-approved protocol with a documented record of who read what.
Quality is designed in,
not inspected afterwards.
Alignment testing
Do reviewers read the protocol the same way?
Adjudication rules
Defined disagreement handling, agreed up front.
Documented escalation
Ambiguous labels escalate rather than get guessed.
Calibration cases
Run before production on a small alignment set.
Double or triple review
On selected cases, where a reference standard is needed.
Reviewer-level tracking
Performance measured per clinician, not per batch.
Recruiting the right names is the starting point, not the method. Which controls apply depends on risk, volume and what the data will be used for — we scope that with you rather than applying a fixed template.
Privacy
and data handling
HIPAA-compliant workflows & ISO 27001
Clinical data usually contains protected health information, so security design matters as much as reviewer quality. We operate HIPAA-compliant workflows under ISO 27001 information-security controls. Data-access method, minimum necessary information and project-specific controls are agreed before annotation begins.
Frequently asked questions
X-ray, CT, MRI and pathology image workflows. Exact modality and task fit is confirmed during scoping.
Yes. Matching goes down to sub-specialty and active clinical context across 100+ specialties and 200+ sub-specialties — that is the point of the network.
Usually. We are tool-agnostic and work inside your environment, subject to technical and security requirements confirmed before launch.
Yes. Quality review of existing annotation and of model outputs is a standard engagement.
Yes. Multi-reader designs with defined disagreement and adjudication rules are built in where the project requires a defensible reference standard.
HIPAA-compliant workflows under ISO 27001 information-security controls, with data-access method and minimum necessary information agreed before annotation begins.
Data type, task definition, reviewer criteria and approximate volume. We come back with the clinician profile, calibration plan and production workflow.
Projects usually start in 5-10 business days.
Your ground truth is only as good
as the clinician who made it.
Tell us what you're building — we'll tell you exactly how we can help. Send the data type, task definition, reviewer criteria and approximate volume, and we'll come back with the clinician profile, calibration plan and production workflow.
Other
services
Managed end to end — recruitment, credentialing, onboarding, training, scheduling, payments and QA. One accountable team.
Expert network access (interviews, surveys)
On-demand access to HCPs for interviews, surveys, advisory input, protocol review and KOL engagement. Any specialty, any geography, any language.
Clinical AI product validation & testing
Structured, multi-country testing of clinical AI tools by active licensed healthcare professionals: reader studies, model validation, benchmark review, LLM and multimodal evaluation. Built for FDA and EU AI Act requirements.