Public Health & Government
Scaling public health surveillance and intervention with trusted AI
Industry Challenge
Public health agencies manage large-scale population health data for surveillance, outbreak detection, resource planning, and policy modelling. AI enables faster outbreak detection, better disease burden estimation, and smarter resource allocation — but these models require labeled population health data and careful evaluation for equity and fairness across diverse populations.
How SCILabel Serves This Industry
Data Collection
We source de-identified notifiable disease surveillance records, community health survey data, vaccination records, and syndromic surveillance datasets from national and county health authority partners.
Data Annotation & Labeling
Our public health professionals and epidemiologists annotate surveillance data with disease classification, outbreak cluster labels, case severity grades, and demographic equity tags. We also structure large-scale health survey datasets for population health AI training.
Data & Model Evaluation
Public health evaluators assess outbreak detection model sensitivity and timeliness against historical outbreak ground truth, test equity of model performance across geographic, demographic, and socioeconomic subgroups, and evaluate health resource allocation model outputs against WHO planning standards.
Annotation Types & Formats
- Disease classification and case severity annotation on surveillance records
- Outbreak cluster labeling on time-series epidemiological data
- Demographic equity tagging for fairness evaluation
- Periapical lesion detection and segmentation
- Health survey response coding and classification
- Vaccination coverage and cold-chain event annotation
Specialist Workforce Tracks
Track 1 (Medical NLP) and Track 5 (AI Safety & Compliance): Public Health Officers, Epidemiologists, Health Informatics Specialists.