Healthcare

The AI systems healthcare runs on

AdwumaTech builds and operates production AI for pharmaceutical companies, clinical AI developers, hospital systems, and population health programs. Diagnostic imaging, clinical documentation, patient-consented data programs, and the evidence layer that carries a system through submission and into clinical use. Full stack, from protocol to deployed system, operated after it ships.

ISO 27001 certified · IRB reviewed protocols · HIPAA supported · GDPR aligned · Ghana Data Protection Act 2012

Clinical Use

Builttoholdinclinicaluse

Clinical AI is built here to the standard it will be held to after deployment. Ground truth traces to the credentialed clinician who produced it. Models are evaluated against the population they will serve and reported by subgroup. Systems are engineered into PACS, EHR, and hospital infrastructure before the first clinical call, and monitored against their deployment baseline after it. Protocol, ground truth, model, system, and operation run as one engagement under one accountable team. Evidence accumulates while the work runs, so submission, institutional review, and post-market surveillance draw on a record that already exists.

Production

Whatrunsinproduction

Diagnostic imaging

Model development and clinical ground truth across radiology, ophthalmology, pathology, and dermatology. X-ray, CT, MRI, ultrasound, fundus photography, dermoscopy, and digital pathology, with DICOM and NIfTI handling throughout. Multi-reader annotation, senior clinical adjudication, and inter-rater reliability reporting are standard on every dataset.

Clinical documentation

Structured extraction from unstructured clinical text. Named entity recognition, relation extraction, temporal reasoning, assertion classification, and negation handling, with terminology normalized to SNOMED CT, ICD-10 and ICD-11, RxNorm, and LOINC. Coverage extends to the abbreviation-heavy, code-switched documentation clinicians write in practice, across English and seven African languages.

Patient-consented data programs

Prospective imaging collection, clinical audio, structured patient interviews, and longitudinal cohort development. Ethics, consent, and governance are addressed at protocol design. Every prospective program runs under IRB or equivalent review with informed consent documented at the point of collection.

Explore data acquisition
Population screening

Screening systems engineered for population-scale deployment, with validation cohorts drawn from the population the program serves and subgroup performance reported as a deliverable.

Health information systems

Extraction, digitization, and interoperability engineering across paper records, legacy archives, and fragmented health information systems. Delivery in HL7 v2, FHIR, or project-specified formats, engineered against the systems already in place.

Model governance

Model inventory, drift monitoring, scheduled revalidation, and subgroup performance tracking. Delivered across every system in the stack, and available as a layer over models built elsewhere.

How The Work Runs

Howtheworkruns

Protocol

The protocol is written before any data moves: inclusion and exclusion criteria, annotation taxonomy, grading framework alignment, reference standard definition, and the ethics review pathway. Success criteria are defined in clinical terms and agreed at this stage.

Ground truth

Annotators are credentialed against the project specification and calibrated before live work begins, with credentials documented. Each artifact is independently annotated by multiple credentialed readers, three or more on high-stakes diagnostic data. Disagreement routes to a senior clinician for adjudication, and outcomes carry documented rationale.

Model development

Supervised fine-tuning and evaluation against held-out sets representative of the intended-use population. Performance is reported by subgroup across geography, skin tone, age, sex, and clinical presentation. Failure modes are documented as a deliverable.

Systems engineering

The model becomes a system. Integration with PACS, EHR, laboratory information systems, and hospital information systems. Latency budgets, throughput targets, failover, and rollback engineered before the first clinical call.

Deployment

The Deployment Readiness Score governs the transition to production. The institution sets the threshold, and the system ships when it clears.

Operation

Drift monitoring, scheduled revalidation, and versioned model updates delivered under the institution's change control. Post-market performance is tracked by subgroup against the deployment baseline.

Evidence

Evidenceaccumulateswhiletheworkruns

Two frameworks govern every system. The Deployment Readiness Score moves a system to production against a scored assessment covering data provenance, evaluation coverage, subgroup performance, documented failure modes, monitoring instrumentation, rollback capability, and clinical governance sign-off. The institution sets the threshold. Nothing ships below it. The Evidence Chain records every model input, output, confidence score, model version, and data access event in a queryable log. Protocol, ethics approvals, annotator credentials, reference standard definition, inter-rater reliability metrics, per-class confusion matrices, adjudication records, subgroup performance, and provenance trail resolve into a single package on demand. That package supports FDA and EMA submission pathways, Predetermined Change Control Plan frameworks for cleared and in-flight devices, institutional review, and post-market surveillance. It exists as the work runs.

Deployment

Deploymenttopology

Cloud, in-country, and on-premises, including air-gapped installations inside hospital networks. Data residency is set per deployment. Institutions with sovereign health data obligations run the full stack inside their own perimeter, with model updates delivered as versioned artifacts under their change control. Integration is API-first, with delivery in DICOM, HL7 v2, FHIR, or project-specified formats. Systems return structured outputs with case-level explainability that a clinical workflow or reporting layer consumes directly.

Deployment Patterns

Deploymentpatterns

Pharmaceutical and biotech

Prospective and retrospective imaging cohorts across the populations a program requires, with annotator credentials and provenance documentation structured for regulatory submission. Deployed across trial imaging endpoints, biomarker development, and translational research.

Clinical AI developers

Ground truth, validation cohorts, and subgroup performance evidence for teams building diagnostic devices, including organizations operating under Predetermined Change Control Plans who need documentation that folds into ongoing submission cycles.

Hospital systems

Diagnostic systems integrated with PACS and EHR, clinical documentation extraction, and legacy archive digitization. Deployed on-premises where health data cannot leave the network.

Population health programs

Screening systems deployed at scale with validation cohorts drawn from the served population, subgroup performance reported continuously, and the evidence trail health authorities require.

Health information system vendors

Clinical language models and extraction pipelines across English and African languages, delivered as components inside the vendor's own platform.

Security And Governance

Securityandgovernance

Encryption at rest and in transit using AES-256 and TLS 1.3. Role-based access control across the platform. Comprehensive audit logging of every model decision and data access event. De-identification applied to project specification. ISO 27001 certified information security management system, with documentation available under NDA for procurement review. Every prospective collection program operates under IRB or equivalent ethics review, with informed consent documented at the point of collection.

ISO 27001

Certified information security management system.

ISO/IEC 42001

AI management system practices across the model lifecycle.

NIST AI RMF

Govern, Map, Measure, Manage mapped to the Deployment Readiness Score.

FDA

Documentation supports submission pathways and Predetermined Change Control Plan frameworks, including annotator qualification records and intended-use population representativeness.

EU AI Act

AI systems that are medical devices under MDR or IVDR are high-risk. The Evidence Chain produces the technical documentation, automatic logging, and human oversight records those obligations require.

EU MDR and IVDR

Evidence packages structured to support client technical documentation and clinical evaluation.

HIPAA

De-identification, access control, and audit trail supporting client-governed US workflows.

GDPR

Aligned for organizations with European data flows.

Ghana Data Protection Act 2012

Consent, purpose limitation, and data subject rights recorded at capture.

IRB and ethics review

Protocol and consent documentation for every prospective program.

Frequently Asked Questions

CommonQuestions

AdwumaTech builds and operates production AI systems for pharmaceutical companies, clinical AI developers, hospital systems, health information system vendors, and population health programs. The work spans diagnostic imaging, clinical documentation, patient-consented data programs, population screening, health information systems, and model governance. AdwumaTech owns the full stack, from protocol design and ground truth through deployment and ongoing operation.
AdwumaTech AI is an applied AI engineering company headquartered in Accra, Ghana, delivering clinically credentialed ground truth and full clinical AI systems. Every annotation, adjudication, and quality review is performed by licensed clinicians credentialed against the project specification and calibrated before live work begins. Clinical operations are anchored by active MOUs with the University of Ghana and Valley View University, with institutional partnerships expanding across West Africa.
AdwumaTech begins every project with a written protocol covering inclusion and exclusion criteria, annotation taxonomy, grading framework alignment, and reference standard definition. Annotators are credentialed and calibrated, each artifact is independently annotated by multiple credentialed readers with three or more on high-stakes diagnostic data, disagreement routes to a senior clinician for adjudication with documented rationale, and every batch ships with inter-rater reliability metrics and per-class confusion matrices.
AdwumaTech ships every system and dataset with the protocol, ethics approvals, annotator credentials, reference standard definition, inter-rater reliability metrics, per-class confusion matrices, adjudication records, subgroup performance reporting, and full provenance trail. The package supports FDA and EMA submission pathways and Predetermined Change Control Plan frameworks for organizations operating cleared or in-flight devices. It is assembled as the work runs.
The Deployment Readiness Score is AdwumaTech's framework governing the transition of an AI system from evaluation to production, scoring data provenance, evaluation coverage, subgroup performance, documented failure modes, monitoring instrumentation, rollback capability, and clinical governance sign-off. The institution sets the threshold. The Evidence Chain is AdwumaTech's audit and explainability layer, logging every model input, output, confidence score, model version, and data access event in queryable form for institutional review, post-market surveillance, and ongoing submission cycles.
AI systems that qualify as medical devices under EU MDR or IVDR are high-risk under the EU AI Act. AdwumaTech's Evidence Chain produces the technical documentation, automatic logging, and human oversight records those obligations require. AdwumaTech maps to the NIST AI Risk Management Framework through the Deployment Readiness Score, applies ISO/IEC 42001 practices across the model lifecycle, holds ISO 27001 certification, and supports client obligations under HIPAA, GDPR, and the Ghana Data Protection Act 2012.
AdwumaTech runs both prospective and retrospective clinical data programs. Retrospective work uses existing data shared under institutional agreements with de-identification applied to project specification. Prospective programs operate under IRB or equivalent ethics review, with informed consent documented at the point of collection and governance addressed at protocol design. Program types include imaging collection, clinical audio, structured patient interviews, and longitudinal cohort development.
AdwumaTech supports DICOM, NIfTI, and digital pathology whole slide imaging, with delivery in HL7 v2, FHIR, or project-specified formats. Clinical NLP outputs normalize to SNOMED CT, ICD-10, ICD-11, RxNorm, and LOINC. Annotation aligns to standard clinical grading frameworks including ETDRS, BI-RADS, TNM, and modality-specific scoring systems.
AdwumaTech systems deploy in cloud, in-country, and on-premises environments, including air-gapped installations inside hospital networks, with data residency configured per deployment. Integration is API-first, connecting to PACS, EHR, laboratory information systems, and hospital information systems. Institutions with sovereign health data obligations run the full stack inside their own perimeter, with model updates delivered as versioned artifacts under their change control.
AdwumaTech delivers clinical AI globally across the patient populations and languages a project requires, with structural depth in African patient populations underrepresented in existing datasets. Clinical language coverage spans English plus Twi, Fante, Ga, Ewe, Hausa, Yoruba, and Swahili. AdwumaTech also maintains mGhana-ST, an open speech translation corpus for Akan, Ewe, and Ga, which won Best African Dataset at Deep Learning Indaba 2026.