Medical imaging annotation, clinical NLP, and patient consented data acquisition for healthcare AI. Credentialed clinical review, regulatory grade documentation, and access to underrepresented African patient populations for clients building AI that has to work across diverse populations.
ISO 27001 Certified · GDPR Aligned · Ghana Data Protection Act 2012 · IRB Reviewed Protocols
Clinical AI has demonstrated real potential across diagnostic imaging, clinical decision support, and workforce augmentation. Scaling that potential into safe deployment is where most programs stall.
Diagnostic AI models trained on US or European populations show measurably different accuracy when deployed elsewhere. As of January 2025, FDA expects AI device submissions to document representativeness of training data for the intended use population.
A bounding box drawn by a non clinician on a chest X-ray is not a substitute for a labeled finding from a board certified radiologist. FDA guidance asks for documentation of annotator qualifications and training.
Critical clinical data sits in unstructured physician notes, discharge summaries, pathology reports, and paper records. Clinical NLP trained only on US English clinical text breaks down on multilingual, code switched, and abbreviation heavy documentation.
A few data points that frame the operating environment for any organization building or deploying clinical AI in 2026.
Less than 5 percent
Share of medical imaging in major public AI training datasets that originates from African patient populations.
DICOM, HL7, FHIR
Healthcare data standards our pipelines support for ingestion, annotation, and delivery.
Credentialed clinicians
Every clinical annotation, adjudication, and quality review is performed by licensed clinicians.
University partnered
Active MOUs with the University of Ghana and Valley View University.
Three service layers for clinical AI training data, supported by credentialed clinical review, ethics oversight where applicable, and documentation built to fold into FDA, EMA, and equivalent regulatory submissions.
Annotated training datasets for diagnostic AI across radiology, ophthalmology, pathology, and dermatology. Support for DICOM imaging formats and coverage across X-ray, CT, MRI, ultrasound, fundus photography, dermoscopy, and digital pathology on request.
Capabilities include classification, bounding box and keypoint annotation, segmentation, multi reader consensus, ground truth adjudication by senior clinicians, and inter rater reliability reporting.
Structured data extraction from unstructured clinical documentation, including named entity recognition, relation extraction, temporal reasoning, assertion classification, and negation handling.
Terminology normalization maps to SNOMED CT, ICD 10 and ICD 11, RxNorm, and LOINC for downstream interoperability with EHRs, HL7 messaging, and FHIR resources.
Custom clinical data collection programs with ethics, consent, and governance addressed from protocol design. Prospective medical imaging collection, clinical audio recording, structured patient interviews, and longitudinal cohort dataset development.
Every collection program operates under IRB or equivalent ethics review, with informed consent documented at the point of collection.
Every clinical dataset moves through a six stage process designed to meet the documentation standards that regulators and institutional review boards expect.
Every project begins with a written protocol covering inclusion and exclusion criteria, annotation taxonomy, grading framework alignment, reference standard definition, and ethics review pathway.
Annotators are credentialed against the project specification and run through calibration sets before live work begins. Credentials, training, and qualifications are documented.
Each clinical artifact is independently annotated by multiple credentialed readers. High stakes diagnostic data carries three or more independent readers.
Disagreement cases route to a senior clinician for ground truth adjudication. Outcomes are documented with rationale.
Every batch ships with measured inter rater reliability metrics, per class confusion matrices, and reviewer level performance reporting where applicable.
Datasets are delivered through encrypted channels in DICOM, HL7, FHIR, or project specified formats, with provenance documentation and full audit trail.
AdwumaTech delivers prospective and retrospective annotated imaging across the populations the project requires, with annotator credentials and provenance documentation for FDA, EMA, and equivalent submissions.
Fit: pharmaceutical companies, biotech, clinical AI vendors, contract research organizations.
We provide annotated training data, validation cohorts, and documentation that lets screening programs deploy AI safely at population scale.
Fit: ministries of health, hospital systems, donor funded screening programs, public private health partnerships.
AdwumaTech delivers clinical NLP training data and model fine tuning across English plus the African languages used in real clinical documentation.
Fit: ministries of health, health information system vendors, surveillance programs, EHR vendors.
Explore African language servicesWe deliver annotation and validation cohort support for clients operating under Predetermined Change Control Plan frameworks, with documentation built to fold into ongoing FDA submission cycles.
Fit: clinical AI vendors with cleared or in flight FDA devices and medical device manufacturers.
Annotation, adjudication, and quality review happen by credentialed clinicians with documented experience in the modality and condition.
Clinical operations are anchored by active MOUs with the University of Ghana and Valley View University, with clinical institution partnerships expanding across West Africa.
We provide training data that represents the patient populations your AI needs to serve, with documented diversity across geography, skin tone, age, and clinical presentation.
Every dataset ships with protocol, ethics approvals, annotator credentials, reference standard definition, IRR metrics, adjudication records, subgroup performance reporting, and provenance trail.
Encryption at rest and in transit using AES 256 and TLS 1.3. Role based access control. Comprehensive audit logging. ISO 27001 certified information security management system.
Every prospective data collection program runs under IRB or equivalent ethics review, with informed consent documented at the point of collection.
We support clients operating across HIPAA, GDPR, and African data protection frameworks. Deliverables include documentation, audit trails, and process controls that support client Part 11 obligations.
Pharmaceutical and biotech teams, clinical AI vendors, medical device companies, hospital systems, ministries of health, public health programs, donor funded health programs, and global enterprises.
Book a call. We walk through where your current training data, annotation pipeline, or model evaluation may be underperforming on the populations or documentation standards your AI needs to meet. If the fit is there, we run a live demo on the same call against sample clinical data from your modality.
