UAE multi-site · Imaging
18 % faster turnaround on critical chest X-ray findings
Clinician adoption above 80 % at 90 days. Full audit trail through PACS. Co-authored adoption paper with the clinical champion (anonymised in public; named under NDA).
Clinical AI · JCI hospitals & UAE health regulators
Clinical AI built for JCI-accredited UAE hospitals — integrated with your EHR, your PACS, and your HIE.
Imaging on MONAI. Sepsis early-warning that nurses actually trust. Patient-flow forecasting on your data warehouse. Arabic clinical NLP. Every model fine-tuned on your corpus before go-live, with a regulatory pack aligned to MOHAP, DoH and DHA, and a clinician-led adoption programme that reaches 78 % adoption at 90 days.
Inference runs in your hospital. Data stays in your hospital.
PACS worklist · re-prioritised
MONAI v1.3
Chest X-ray · CR-22988Priority · 0.96
Bounding box: right upper-lobe opacity · triage: priority
- CR-22921 · chestroutine
- CT-22919 · headroutine
- MG-22914 · mammoscreening
Inference
On-prem · NVIDIA Clara
HIE
Malaffi / NABIDH consent
9
UAE healthcare clients served
78%
Clinician adoption at 90 days (playbook deployments)
−24%
Sepsis alert-volume reduction at constant sensitivity
100%
PHI workloads inside UAE borders
The painful problem
Radiology backlog up 38 %. ICU sepsis alerts at 70 % false-positive. Malaffi data unused.
The on-call sub-specialty roster cannot cover after-hours reads. Nurses are tuning out the deterioration alerts. The data warehouse holds Malaffi feeds that no clinically actionable model touches. The last EHR-bundled AI pilot lost momentum after the champion clinician moved on.
Failure mode 1
Clinician adoption
The median UAE hospital AI pilot reaches 23 % clinician adoption at 90 days. The system stays in the building, the clinicians work around it. Adoption is the only metric that matters past month two.
Failure mode 2
EHR fragility
Heavily customised Cerner and Epic instances break under naive integrations. A worklist disruption is a safety event before it is an IT ticket.
Failure mode 3
HIE consent
Malaffi and NABIDH governance is non-negotiable. Consent flows, de-identification rules, and the opt-in / opt-out pattern have to be documented before any patient row is queried.
Use-case grid
Nine clinical and operational plays we have shipped inside UAE hospitals.
Imaging, sepsis, flow, Arabic clinical NLP. Each fine-tuned on your corpus, validated against held-out data, and supported by a six-week clinician adoption sprint.
Chest X-ray triage
MONAI-based model on PACS with worklist re-prioritisation. UAE multi-site reference: 18 % faster turnaround on critical findings, clinician adoption above 80 % at 90 days.
18 % faster · 80 % adoption
Head CT haemorrhage detection
Pre-trained model fine-tuned on the hospital corpus. Reading-room priority tag, full audit trail through PACS.
Mammography density & lung-nodule
Tertiary-screening assist with confidence calibration on the hospital population. Documented for CE MDR Class IIa positioning.
Sepsis early warning
Tertiary hospital reference: 24 % alert-volume reduction with no loss in sensitivity, validated on 18 months of historical ICU data.
−24 % alert volume
Readmission prediction
Hospital-specific 30-day readmission model with SHAP-based explainability for case-management teams.
Patient flow & OT utilisation
Quality-and-flow reference: OT utilisation uplift of 11 percentage points across two surgical departments. ED LWBS forecasting alongside.
+11 pp OT utilisation
Arabic clinical NLP
Fine-tuned AraBERT-v2 clinical head on de-identified Arabic notes for problem-list extraction, family history, and social determinants.
Ambient documentation
In-room dictation with structured note drafting; clinician signs the note rather than writes it. Bilingual Arabic / English handling.
Pathology slide triage
Whole-slide image triage on NVIDIA Clara, integrating upstream of the digital pathology viewer rather than replacing it.
Integration mechanism
Inside your EHR. Inside your PACS. Inside your hospital.
MONAI on NVIDIA Clara. HL7 FHIR R4 with named connectors for Cerner / Epic / TrakCare. CDS Hooks for in-workflow inference. Malaffi and NABIDH consent flows documented for legal.
Imaging stack
MONAI · NVIDIA Clara · DICOM
EHR connectors
Cerner · Epic · TrakCare
HIE patterns
Malaffi · NABIDH consent
Arabic clinical NLP
AraBERT-v2 clinical head
Layer 1
EHR & PACS
Cerner / Oracle Health, Epic, InterSystems TrakCare, SystmOne on the EHR side; GE Centricity, Philips IntelliSpace, Sectra, Carestream on PACS. HL7 ORM / ORU on the imaging worklist.
Layer 2
HIE & consent
Malaffi (Abu Dhabi) and NABIDH (Dubai) consent flows with documented opt-in / opt-out. De-identification rules for training vs inference are explicit.
Layer 3
Model serving
MONAI on NVIDIA Clara appliance inside the hospital network. CDS Hooks for in-workflow inference. Arabic clinical NLP head where notes are bilingual.
Layer 4
Regulatory pack
Model card, intended-use statement, performance-monitoring plan, bias-and-equity review aligned to MOHAP medical-device guidance and FDA 510(k) / CE MDR Class IIa where applicable.
Layer 5
Clinical adoption
Six-week clinical-champion shadowing per use case. Threshold tuning with the informatics team. Quarterly clinical validation reports to the quality committee.
Regulator & standards
Aligned to the reviewers in the room.
DoH Abu Dhabi. MOHAP. DHA. JCI surveyors. Every model ships with a clinical evidence pack the medical director and quality committee can read on first pass.
MOHAP / DoH / DHA
Model card, intended-use statement, performance-monitoring plan, and a bias-and-equity review aligned to MOHAP medical-device guidance. The same pack has passed pre-market review for a UAE hospital group.
FDA 510(k) / CE MDR
Imaging models targeted at Class IIa pathways structure documentation accordingly. Substantial equivalence reasoning and clinical evaluation are written for the assessor, not the marketing brief.
JCI alignment
Clinical decision support effectiveness, alert-fatigue handling, and human-in-the-loop checkpoints are documented to JCI expectations on clinical informatics governance.
ISO & HIPAA posture
ISO 13485 alignment statement for clinical-software development, ISO 27001, SOC 2 Type II, HIPAA-aligned controls for international affiliates. Partnerships with NVIDIA Inception, Microsoft for Healthcare, Snowflake Healthcare & Life Sciences.
Reference engagements
Three anonymised clinical outcomes.
Tertiary hospital · ICU
24 % sepsis alert-volume reduction with constant sensitivity
Validated on 18 months of historical ICU data with cohort analysis across age and comorbidity. Quarterly clinical validation in place.
Quality & flow
+11 pp OT utilisation across two surgical departments
Patient-flow forecasting on the hospital data warehouse. ED LWBS prediction alongside. No EHR modification required.
Differentiation
Brocode vs the four archetypes already on your evaluation.
EHR-vendor module, point clinical-AI vendor, hyperscaler healthcare practice, or your CMIO in-house team. Where each fits — and where it does not.
| Capability | Brocode | EHR-vendor module (Cerner / Epic / Oracle Health) | Point AI vendor (Aidoc / Lunit / Qure.ai) | Hyperscaler healthcare practice | In-house CMIO build |
|---|---|---|---|---|---|
| On-premise inference inside the hospital | Partial | Partial | |||
| HL7 FHIR R4 + CDS Hooks integration | Partial | ||||
| Malaffi / NABIDH consent flow documented | Partial | Partial | |||
| Arabic clinical NLP (AraBERT-v2 clinical head) | |||||
| Clinician-led 6-week adoption programme | Partial | ||||
| Fine-tuned on hospital corpus before go-live | Partial | ||||
| MOHAP medical-device documentation pack | Partial | Partial | |||
| Cross-modality bench of pre-trained models | Partial | ||||
| No telemetry to vendor; hospital owns the model |
Free download
Clinical AI Adoption Playbook for JCI Hospitals in the UAE
A 30-page field guide covering the eight clinician-adoption failure modes we observed across UAE deployments, the Malaffi / NABIDH integration pattern with consent flow diagrams, the MOHAP medical-device regulatory pathway, and a worked example of in-workflow CDS for sepsis. Headline figure: the median UAE hospital AI pilot reaches 23 % clinician adoption at 90 days; deployments using this playbook reach 78 %.
- Regulatory pathway (DOH, MOHAP, DHA)
- Malaffi / NABIDH integration patterns with consent flow
- HL7 FHIR pipelines and CDS Hooks worked example
- MONAI medical imaging stack reference architecture
- Clinician change-management playbook
- Sample IRB submission
Clinical & integration FAQ
The eight questions every quality committee raises.
If yours is not here, raise it in the form below. We answer in writing before the first call.
Every imaging and clinical model is fine-tuned on the hospital's own corpus before go-live; the regulatory pack includes performance metrics on the hospital's population, not a US/EU benchmark transplanted in. For sepsis early-warning, validation runs on 18 months of historical ICU data with cohort analysis across age, comorbidity, and care-setting. Clinician adoption data — not just AUC — is the headline metric we report.
Start the conversation
Request a clinical AI workflow review with our healthcare lead.
Tell us the facility type, the regulator, the EHR, and the priority use case. A senior engineer comes back within one business day with a shape, a team, and a first conversation.
Prefer WhatsApp? Message our healthcare lead directly.
Continue exploring
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ISO 13485 alignment, ISO 27001, SOC 2 Type II, MOHAP medical-device pathway evidence.
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