🏥 Healthcare AI - Clinical Intelligence Systems

Medical AI that survives
FDA, MDR and the medical-affairs reviewer

AI systems for hospitals, payers and medtech - every recommendation explainable, every model documented, every event captured in an audit trail. Built for HIPAA, HITECH, FDA AI / ML SaMD, EU MDR and IL Patient Rights Law.

Consultation call
⚠️ Regulated domain

Healthcare AI is not a black box

Every decision needs an explanation - for the regulator, for the user and for the internal auditor.

100%
Explainable Decisions
HIPAA + HITECH
PHI handling
FDA SaMD
ready
MDR-aware
design
⚙️ Healthcare AI solutions

End-to-end AI for the sector

🩺

Clinical Decision Support

Recommendation engines for diagnosis, triage and treatment.

  • Differential-diagnosis suggestion with provenance
  • Triage scoring with sensitivity / specificity reported
  • Latest guidelines via RAG (NICE, USPSTF, IL MoH)
  • Human-in-the-loop on every decision
📚

Medical RAG

Retrieval-augmented generation over your clinical guidelines, protocols and SOPs.

  • Citation back to source paragraph
  • PHI redaction at retrieval and at generation
  • Versioned guideline store with provenance
  • Multi-language (EN / HE / RU)
🖼️

Imaging Triage

Computer-vision triage and second-opinion overlays.

  • Radiology / pathology / dermatology models
  • Saliency maps for clinician review
  • AUC / sensitivity / specificity reported
  • Calibrated to clinic prevalence
📑

Claims Automation

Payer-side claims adjudication with explainable denials.

  • ICD / CPT extraction
  • Prior-authorization automation
  • Denial reason codes for appeal
  • Fraud / waste / abuse detection
🛡️

PHI Privacy and Security

Designed for HIPAA / HITECH controls end-to-end.

  • PHI de-identification (Safe Harbor + Expert Determination)
  • Encryption at rest + in transit
  • Access logs + minimum-necessary access
  • BAAs with model providers
🔐

Model Governance

Documentation, monitoring and control of every clinical model.

  • Model card per Mitchell et al. 2019
  • Drift detection on clinical features
  • Champion / Challenger flow
  • Reportable adverse-event tracking
📋 Regulation

Built for every regulator in the room

🇺🇸

US: HIPAA, HITECH, FDA SaMD

HIPAA Privacy + Security Rule, HITECH breach notification, FDA AI / ML-based Software as a Medical Device action plan, 21st Century Cures interoperability.

🇪🇺

EU: GDPR-Health, MDR, EU AI Act

GDPR Article 9 special-category health data, EU MDR for software-as-device classification, EU AI Act Article 6 / 9 / 10 / 13 / 14 / 15 for high-risk medical AI.

🇮🇱

IL: Patient Rights, MoH Digital Health

Israel Patient Rights Law 5756, MoH digital-health guidance, Israeli Privacy Protection Authority + INCD security baselines.

📊 Industry benchmarks

What to expect from Healthcare AI that works in production

Most models fail not in the demo - but in production: drift, audit, integration. Here is what the public industry research reports and where our architectural contribution comes in.

🩺

Clinical RAG accuracy

What the industry reports: Clinical RAG with retrieval-then-generate over current guidelines reports 85-92% answer-with-correct-citation, vs ~70% for plain LLM (BMJ AI 2024 systematic review).

Where we come in: Citation back to source paragraph, PHI redaction at retrieval + at generation, versioned guideline store - so every clinician answer is traceable.

  • Citation back to source paragraph (no answer without source)
  • PHI redaction at retrieval and at generation
  • Versioned guideline store - reviewable diff over time
  • Built to update with new guideline releases without retraining
🖼️

Imaging triage

What the industry reports: Triage AI models on chest X-ray + CT report AUC 0.90+ for rule-out workflows when calibrated to local prevalence (Lancet Digital Health 2024).

Where we come in: Calibration to clinic prevalence, saliency maps for clinician review, sensitivity / specificity reported per cohort - presentable to a model committee.

  • AUC and sensitivity / specificity reported per cohort, not in aggregate
  • Calibrated to clinic prevalence (not vendor benchmark)
  • Saliency maps for clinician review
  • Reportable adverse-event tracking
📑

Claims automation

What the industry reports: Payer-side claims auto-adjudication with explainable denials reduces appeal cycle by 30-45% (Bain healthcare ops 2024).

Where we come in: ICD / CPT extraction, prior-authorization automation, denial reason codes for appeal, fraud / waste / abuse detection - end-to-end claims flow with audit trail.

  • Denial reason codes for appeal (every denial explainable)
  • Prior-authorization automation with rule-based fallback
  • Fraud / waste / abuse detection
  • Built for HIPAA / HITECH end-to-end

* SLAtech, since 2004. We have worked with hospitals, payers and medtech vendors in 14 countries. Commercial examples available on a consultation call.

Ready to build Healthcare AI that survives the regulator?

30-minute consultation call - we map the clinical use cases, propose an architecture, and quantify ROI.