+VLFBERHT+ | Healthcare
Clinical AI that
clinicians can trust.
AI-assisted diagnostics are transforming care delivery. A hallucinated drug interaction or fabricated clinical reference can harm patients. Ulfberht verifies every clinical AI output before it reaches the care team -- with documented evidence, not AI self-assessment.
The Problem
Healthcare AI has a verification gap that
clinical teams cannot close manually.
Clinical AI systems generate diagnostics, medication recommendations, and treatment plans at a speed and volume no oversight process was designed to handle. The failure modes are specific, documented, and directly tied to patient outcomes.
Risk 01
Diagnostic Overconfidence
AI diagnostic tools present findings with uniform confidence regardless of actual certainty. A 55% probability finding looks identical to a 98% probability finding. Clinicians cannot calibrate their review against what the model actually knows.
Observed across radiology AI, pathology assistants, and clinical decision support systems.
Risk 02
Drug Interaction Hallucination
AI systems fabricate drug interactions that don't exist in validated pharmacological databases, or miss interactions that do. Both failure modes have direct patient safety implications. The dangerous failure is the missed interaction -- the one the model skips entirely.
Documented in LLM-assisted prescribing and discharge medication reconciliation workflows.
Risk 03
Clinical Reference Fabrication
AI cites clinical trials, treatment guidelines, and dosing protocols that do not exist. In healthcare, a fabricated reference doesn't just fail a footnote check -- it changes treatment decisions, informs consent conversations, and ends up in the patient record.
Confirmed across clinical summary generation, treatment recommendation, and patient discharge AI.
How It Works
Verification runs in your
clinical workflow, not outside it.
Ulfberht intercepts every AI output before it surfaces to the care team. Each claim is classified, source-traced, and scored against verified clinical databases. The result is a verified output with an attached audit record -- not a second opinion from another AI.
Output Interception
AI output is routed through Ulfberht's verification layer before reaching the EHR or care team interface.
Claim Extraction and Classification
Each factual claim is extracted, typed (diagnostic, pharmacological, citation, protocol), and assigned a verification tier.
Source Verification Against Clinical Databases
Claims checked against PubMed, Cochrane, FDA databases, AHA/ACC guidelines, and pharmacopoeial references.
Audit Record Generation
Immutable verification record attached to every output. Flagged claims require documented clinician acknowledgment before action.
Capabilities
Six verification layers tuned for clinical workflows.
Every capability is built around healthcare-specific failure mode libraries, clinical database integrations, and compliance reporting formats designed for HIPAA environments and FDA SaMD documentation requirements.
Diagnostic Confidence Scoring
Every diagnostic output receives a granular confidence score derived from evidence quality, source recency, and consistency across clinical databases -- not AI self-assessment. Clinicians see exactly how much weight each finding can bear.
Radiology / Pathology / Clinical Decision Support
Drug Interaction Verification
AI-generated pharmacological recommendations cross-referenced against validated drug databases in real-time. Catches fabricated interactions, missed contraindications, and dosing errors before they reach the prescribing interface.
Prescribing / Reconciliation / Discharge
Clinical Citation Tracing
Every cited study, guideline, and clinical trial verified against PubMed, Cochrane Library, FDA databases, and AHA/ACC/ACOG guideline repositories. Fabricated references are caught and flagged before they enter clinical documentation.
Evidence-Based Medicine / Literature Review / CDS
EHR Integration
Native integration with Epic, Cerner, and HL7 FHIR-compatible systems via SMART on FHIR authentication. Verification results and audit records flow directly into clinical workflows without disrupting care team interfaces or existing order entry processes.
Epic / Cerner / HL7 FHIR / SMART on FHIR
Audit Trail
Complete, immutable verification record for every AI-assisted clinical decision. Every verification step, flag acknowledgment, and clinician override is documented with timestamps, user IDs, and verification state -- structured for regulatory review in environments designed for HIPAA compliance.
Compliance / Legal Defensibility / Incident Review
Clinical Action Classification
Pre-execution oversight for every AI-recommended clinical action. Treatment changes, medication adjustments, diagnostic orders, and referral recommendations are classified by risk tier before execution. High-tier actions require documented clinician authorization with verification evidence.
Treatment / Medication / Diagnostics / Referral
Use Cases
Where healthcare teams deploy Ulfberht.
Three scenarios where verification is not optional -- where the gap between an AI output and a verified clinical decision has direct patient consequences.
Use Case 01
Radiology AI Verification
A large health system routes all AI-generated radiology reads through Ulfberht before surfacing them in the radiologist's worklist. Each finding is scored by confidence tier, cross-referenced against prior imaging reports in the patient record, and checked for diagnostic consistency. Findings below 80% confidence are held for attending radiologist review before any downstream action.
Confidence-tiered worklist routing
Cross-referenced against prior studies
Audit record per finding per patient
Use Case 02
Clinical Decision Support
An academic medical center integrates Ulfberht into its LLM-powered clinical decision support system. When the CDS recommends a treatment protocol, Ulfberht verifies every cited guideline, checks recommended medications against the patient's current drug regimen for interactions, and generates a verification summary that the ordering physician reviews before proceeding.
Guideline citation verification at point of care
Patient-specific drug interaction check
Physician acknowledgment before order entry
Use Case 03
Patient Discharge Summaries
A regional hospital network uses LLMs to generate patient discharge summaries at scale. Before any summary is signed, Ulfberht verifies every medication listed matches the patient's administered record, checks follow-up instructions against current care protocols, and flags any diagnostic claims inconsistent with the inpatient record. Inconsistencies surface to the discharging physician before the document is finalized.
Medication list matched to administration record
Protocol-consistent follow-up verification
Pre-signature physician review gate
The Evidence
Built on verified behavioral research,
not vendor benchmarks.
The Ulfberht verification engine is built on a documented research program covering 150 failure modes across 14 behavioral categories, validated across 30+ AI models in controlled experiments. Healthcare-specific failure mode libraries are maintained as clinical AI systems evolve.
View the Research →150
Failure Modes
14 behavioral categories
930+
Validation Tests
Across 30+ AI models
6
Verification Layers
Between AI and care team
100%
Error Propagation
Unverified multi-agent chains
Environment
Built for HIPAA
Architecture designed around administrative, physical, and technical safeguard requirements
Regulatory
FDA SaMD Pending
Audit and documentation structures designed for Software as a Medical Device guidance (certification pending)
International
Designed for EU MDR
Documentation and traceability patterns mapped to EU Medical Device Regulation requirements
Interoperability
HL7 FHIR Compatible
Verification results delivered via FHIR resources for native EHR integration
+VLFBERHT+ | Healthcare
Verify your clinical AI.
Before it reaches the care team.
Schedule a technical deep-dive with our healthcare team. We'll demonstrate verification running on clinical AI workflows similar to yours -- diagnostic reads, CDS recommendations, discharge documentation.