Clinical judgment
Labels and references reflect diagnostic reasoning, treatment logic, and clinical risk — not surface-level text patterns.
Access physician-led clinical labels, reference answers, rubrics, and evaluation datasets created by people who understand medicine, not just labeling tools.
In medicine, an incorrect label is not a cosmetic issue — it teaches the model the wrong clinical signal. Annotating symptom severity, treatment appropriateness, diagnostic reasoning, or patient safety risk requires clinical judgment, familiarity with medical evidence, and awareness of how care is actually delivered. Every annotation project is therefore staffed with qualified physicians.
Labels and references reflect diagnostic reasoning, treatment logic, and clinical risk — not surface-level text patterns.
Shared rubrics, structured guidance, and coordinated physician cohorts keep annotations consistent across large datasets.
Annotation can reflect the clinical contexts, disease patterns, and healthcare settings relevant to your data.
Annotation work is defined project by project. Common outputs include:
Physician-applied labels on clinical text, dialogue, imaging reports, and medical records-style data — grounded in real clinical understanding rather than general labeling.
Expert-written model answers that define what a correct, complete, and clinically safe response looks like for a given prompt or case.
Evaluation rubrics and grading criteria authored by clinicians, so automated and human scoring measure what actually matters medically.
Structured datasets for benchmarking and regression testing of medical-AI systems, built to project-specific clinical requirements.
Flags for unsafe, misleading, incomplete, or clinically inappropriate content, applied by people trained to recognize clinical risk.
Annotation and expert review across supported languages, including English and Amharic, with coverage expanding as the network grows.
Physician-annotated data supports medical-AI teams across training, evaluation, and safety work. Requirements, specialty mix, and language coverage are defined per project.
Annotation projects draw on a qualified physician network. Experts complete a structured qualification process covering professional credentials, clinical expertise, safety judgment, and project readiness before participating. Cohort, workflow, and delivery coordination remain under one professional relationship rather than multiple individual contractors.
Credential-verified physicians, structured through the Linco Expert Readiness Framework.
Specialty, language, and capacity matched to your dataset and guidelines.
Coordinated workflows and delivery keep your team focused on the model.
Learn more about how experts are qualified and the broader medical-AI expertise Linco provides.
Share the data type, annotation guidelines, specialty mix, language requirements, and volume.
Linco assesses current expert availability and confirms whether the requirement can be supported.
A managed physician cohort produces the annotation work to your specification and timeline.
For the full range of expert services, see Solutions.
Tell us about your dataset, guidelines, specialty and language requirements, and the capacity you are looking for.