Clinical-Grade Training Data for Healthcare AI
Medical imaging annotation, clinical NLP, and RLHF—built for regulatory submission and clinical deployment, with unique access to African patient populations and clinical contexts.
Schedule a ConsultationHealthcare AI Demands Healthcare Expertise
AI has demonstrated the potential to transform healthcare delivery—automated screening, clinical decision support, workforce augmentation. The evidence from pilots is clear.
Scaling that potential into health system operations is where most programs stall.
Training Data Mismatch
AI models trained on North American and European populations underperform in African health systems. Disease prevalence, clinical presentation, and imaging quality differ. Models need local data.
Specialist Capacity Constraints
Screening programs generate imaging volumes exceeding specialist capacity. AI triage can help—but only if underlying models are trained on representative data.
Health Information Fragmentation
Critical data is trapped in unstructured clinical notes and paper records. Manual abstraction cannot scale. Decision support depends on structured data most systems cannot extract.
End to End Healthcare AI Data Services
Medical Image Annotation
Training datasets for diagnostic AI across radiology, ophthalmology, pathology, and dermatology. Annotation aligned with ETDRS/ICDR for diabetic retinopathy, BI-RADS for breast imaging, and domain-appropriate grading systems.
Differentiator: Datasets representing African populations—skin tones, disease prevalence, and imaging conditions absent from public benchmarks.
Clinical NLP & Structured Data Extraction
Structured data extraction from unstructured clinical documentation. Named entity recognition, relation extraction, temporal reasoning, and assertion classification. Terminology normalization to SNOMED-CT, ICD-10, RxNorm, and LOINC.
Differentiator: Multilingual annotation including African languages—enabling NLP for community health documentation where English-only models fail.
Data Acquisition Programs
Custom clinical data collection with ethics, consent, and governance addressed from protocol design. Prospective imaging collection, clinical audio recording, and patient-consented dataset development for AI initiatives.
Partnerships: Clinical institutions across Ghana and West Africa providing access to populations underrepresented in global datasets.
Differentiator: Training data that represents your patient population—not proxies from other contexts.
Where Clinical Expertise Meets Training Data
Population-Level Screening
AI-powered screening at scale—diabetic retinopathy, tuberculosis, cervical cancer. Early detection that saves lives and reduces treatment costs.
Learn moreHealth Information Systems
Transform unstructured clinical documentation into actionable intelligence—surveillance, quality measurement, decision support.
Learn moreWorkforce Augmentation
AI-assisted documentation, task shifting support, and telemedicine enablement—reducing administrative burden on frontline health workers.
Learn moreBuilt for Health Systems Deploying AI in African Contexts
On-Continent Clinical Expertise
Ghana-based operations with partnerships across West African physicians, nurses, pharmacists, and allied health professionals. Annotation by credentialed clinicians who understand local disease burden and workflows.
Population-Representative Datasets
Most public medical AI datasets are from North American and European populations. We provide training data representing the populations your health system serves.
Multilingual Capability
Health systems across Africa operate in multilingual environments. Our annotation includes Akan, Hausa, Yoruba, and other African languages—enabling AI where English-only models fail.
Donor & Ministry Engagement
We understand procurement processes, reporting requirements, and accountability frameworks. Data deliverables documented for program evaluation and sustainability planning.
We partner with hospital systems, health ministries, and donor-funded programs building AI capabilities for the populations they serve—providing the clinical expertise, local context, and population-representative datasets that global vendors cannot.
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