Healthcare

Clinical Training Data Annotated By Credentialed Clinicians, with the Population Diversity Your Models Require.

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

The Challenge

Why Healthcare AI Demands Better Training Data

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.

Population mismatch in training datasets

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.

Clinical credentialing in annotation

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.

Clinical documentation locked in unstructured text

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.

By The Numbers

The Clinical AI Training Data Landscape By The Numbers

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.

What We Deliver

What We Deliver

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.

Medical imaging annotation

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.

Clinical NLP and structured data extraction

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.

Patient consented data acquisition

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.

Explore data acquisition
Regulatory Grade Process

How We Deliver Clinical Data At Regulatory Grade

Every clinical dataset moves through a six stage process designed to meet the documentation standards that regulators and institutional review boards expect.

Stage 1, protocol and ethics design

Every project begins with a written protocol covering inclusion and exclusion criteria, annotation taxonomy, grading framework alignment, reference standard definition, and ethics review pathway.

Stage 2, clinical annotator credentialing and calibration

Annotators are credentialed against the project specification and run through calibration sets before live work begins. Credentials, training, and qualifications are documented.

Stage 3, multi reader annotation

Each clinical artifact is independently annotated by multiple credentialed readers. High stakes diagnostic data carries three or more independent readers.

Stage 4, senior clinical adjudication

Disagreement cases route to a senior clinician for ground truth adjudication. Outcomes are documented with rationale.

Stage 5, inter rater reliability and quality reporting

Every batch ships with measured inter rater reliability metrics, per class confusion matrices, and reviewer level performance reporting where applicable.

Stage 6, secure delivery and audit trail

Datasets are delivered through encrypted channels in DICOM, HL7, FHIR, or project specified formats, with provenance documentation and full audit trail.

Use Cases

How Healthcare AI Teams Deploy AdwumaTech

Pharmaceutical research and clinical AI development

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.

Population level screening program deployment

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.

Health information systems and clinical surveillance

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 services
Adaptive AI and ongoing model updates

We 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.

Difference

What Makes AdwumaTech Different

Credentialed clinical review at every stage

Annotation, adjudication, and quality review happen by credentialed clinicians with documented experience in the modality and condition.

Global delivery from operations in Accra

Clinical operations are anchored by active MOUs with the University of Ghana and Valley View University, with clinical institution partnerships expanding across West Africa.

Population representative clinical data

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.

Submission grade documentation built in

Every dataset ships with protocol, ethics approvals, annotator credentials, reference standard definition, IRR metrics, adjudication records, subgroup performance reporting, and provenance trail.

Security

Security, Ethics, and Compliance

Information security

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.

Patient data ethics and consent

Every prospective data collection program runs under IRB or equivalent ethics review, with informed consent documented at the point of collection.

Regulatory alignment for client workflows

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.

Who we work with

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.

Frequently Asked Questions

Common Questions

We deliver three things together that most vendors deliver only one or two of: credentialed clinical annotation, regulatory grade documentation built into every deliverable, and a structural advantage on population diversity through our clinical operations and partnerships across West Africa. We work with clients globally, with African population coverage as an additional capability.
We work with a curated network of credentialed clinicians, including physicians, radiologists, ophthalmologists, pathologists, and other licensed specialists, engaged through professional service agreements aligned to project specifications. The network is anchored by active MOUs with the University of Ghana and Valley View University. Every annotator is credentialed, calibrated, and documented.
Both. Retrospective annotation projects use existing data shared under institutional agreements with appropriate de-identification. Prospective collection programs operate under IRB or equivalent ethics review with informed consent at the point of collection.
Annotation aligns to standard clinical frameworks including ETDRS, BI RADS, TNM, and modality specific scoring systems on request. Support includes DICOM, NIfTI and digital pathology whole slide imaging on a per project basis. Clinical NLP outputs map to SNOMED CT, ICD 10, ICD 11, RxNorm, and LOINC, with HL7 and FHIR delivery options.
AdwumaTech holds ISO 27001 certification covering our information security management system. We align with GDPR for European data flows and the Ghana Data Protection Act 2012 for in country data. We support HIPAA governed US workflows and FDA submission pathways through documentation, de-identification, and process controls.
We deliver clinical annotation for clients globally, across the patient populations and languages the project requires. Our structural strength is access to African patient populations underrepresented in global datasets, with active coverage in Ghana and expanding partnerships across West Africa. Clinical NLP covers English plus African languages including Akan, Hausa, Yoruba, and Swahili.
Yes. Standard pilot structure runs 4 to 8 weeks against a defined sample of your annotation or data acquisition need, with measured inter rater reliability and quality benchmarks reported at completion. Most pilots convert to production data programs within one quarter of completion.

See where the gap is in your clinical AI training data

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.