Medical AI Compliance Evidence Review
Review de-identified clinical-AI output evidence for safety and compliance concerns before qualified human clinical review.
Who this is for
- Hospital AI governance leads responsible for controlled review of clinical-AI outputs.
- Medical-AI safety and compliance managers reviewing de-identified evidence before clinician use.
- Clinical technology teams preparing bounded human-review workflows.
The problem you already have
A clinically plausible output can still contain a safety-critical contradiction
Part 117 distinguishes medical-AI problems such as contradictory recommendations, laterality errors, invented measurements and unsupported findings. For this product, that supports a bounded evidence review focused on whether the supplied output contains a condition that should be flagged or blocked before qualified human use.
Safety review needs traceable evidence, not just a model confidence score
A reviewer needs to know which supplied clinical-AI output was checked, what de-identified context was considered and why the result was classified as acceptable, review-required or blocked. A standalone confidence score cannot provide that evidence trail.
Medical-AI assurance must stop before diagnosis, treatment or certification authority
The current surface is a functional registered medical-compliance capability for supplied clinical-AI evidence. It supports human review only; treatment advice, autonomous diagnosis, medical-device certification, regulatory approval and clinician replacement remain explicitly outside scope.
Real-World Operational Scenario: The Clinical Output That Looked Coherent Until Two Recommendations Contradicted Each Other
Scenario: A de-identified clinical-AI output contains two individually plausible recommendations that cannot both be correct under the supplied context.
A hospital AI-governance team wants to detect that conflict before the output reaches a clinician-facing workflow.
WITHOUT this gate: The content may appear fluent and plausible enough to pass an initial product review, leaving the contradiction for a clinician or safety reviewer to discover later.
WITH this gate: The exact supplied output and bounded de-identified context are reviewed under one evidence contract. Contradictory, degenerate or otherwise safety-relevant conditions remain tied to the reviewed content, and the result is routed for qualified human review rather than treated as a clinical decision.
What BivectorAI Provides
Clinical Output Consistency Review
Evaluates the supplied clinical-AI output for bounded internal consistency and safety-relevant contradictions before it enters a human-controlled workflow.
Safety-Condition Detection
Uses Part 117 technical lineage for issues such as contradictory recommendations, unsupported measurements, laterality conflicts and other review-required conditions without presenting the result as diagnosis.
Input-Linked Medical Evidence Record
Produces a traceable record tying the reviewed AI output, de-identified context, findings and review status together for governance, safety and audit workflows.
Qualified-Clinician Decision Boundary
Keeps treatment, diagnosis, clinical action, device certification and regulatory approval under qualified human and regulatory authority.
Prove the Gate Before Broader Integration
Back-test the evidence gate against frozen historical clinical-AI outputs with known qualified-review disposition.
Freeze the supported output format, de-identified context fields, safety-condition rules, review-required/blocked criteria and evidence-record schema before revealing the historical reviewer outcome. Broader integration is justified only if the same contract reproducibly surfaces material safety inconsistencies while preserving the clinician-owned decision boundary.
What you provide
Bounded clinical-AI output evidence and only the de-identified clinical context required for the agreed safety/compliance question. Patient identifiers must not be supplied, and treatment authority or autonomous clinical decision authority are not required.
What BivectorAI actually hands you
Safety / consistency findings
A bounded review showing where the supplied clinical-AI output is internally consistent and where safety-relevant issues remain.
Review-required / blocked conditions
An explicit record of conditions that should not progress without qualified human review under the agreed evaluation contract.
Audit evidence record
An input-linked record tying the reviewed output, de-identified context, findings and review status together.
Compliance / evidence-gap findings
A clear list of missing or unresolved evidence points that remain for clinical, safety, compliance or regulatory professionals.
What we will never ask you for
Clinical-AI review has stricter data and authority boundaries than ordinary software QA, so the evidence layer must remain bounded and human-controlled.
- Raw PHI when prohibited by the agreed contract.
- Treatment authority.
- Autonomous clinical decision authority.
- A claim of medical-device approval or clinical certification.
How it actually runs
Start with a bounded evaluation
Fixed scope / quoted per engagement. Inputs, outputs, evidence boundary and acceptance criteria are agreed before work begins.
We do not open with an annual license. If the bounded medical-AI evidence review does not establish useful findings for the agreed safety or compliance question, there is no reason to move into a broader commercial commitment.
A functional registered medical-compliance surface exists for supplied clinical-AI output evidence. The capability supports human review; it is not treatment advice, autonomous diagnosis, medical-device certification, regulatory approval or a substitute for qualified clinicians.