Proof for every
clinical decision.
Verifiable AI and post-quantum security for healthcare — proof you can check, not trust you're asked to extend.
RankShieldMD produces cryptographic, externally-anchored evidence that a clinical-AI decision came from an un-tampered model on clean data, that every record access was made by a verified identity, and that connected and implanted devices carry post-quantum-safe credentials. It never sees protected health information — and it is not a medical device.
The unprovable
AI decision.
Models now triage, flag, draft orders — and in early pilots, prescribe. When a decision is later questioned, no one can prove what happened: was it the model you validated, or a drifted, swapped, or poisoned one? Logs can be edited. Dashboards ask for trust. Every AI decision in medicine needs a receipt.
Prove the decision.
The device. The access.
Three things no incumbent proves today — each sealed to one verifiable fabric, each checkable independently, none of it touching PHI. Device-security tools inventory and segment; governance tools document risk. RankShieldMD proves the decision.
Secure into
the quantum age.
Implants and connected devices outlive the cryptography they ship with — and can't be recalled. RankShieldMD gives them post-quantum identity and rotates their keys in the field, no recall. Against "harvest now, forge later," a decade of evidence stays defensible.
Don't trust it.
Verify it.
Anyone can claim their clinical AI is safe. RankShieldMD lets you check. Every decision becomes a cryptographic receipt — tamper-evident, and provable independently. Tamper with the data and watch verification fail.
Put your medicine
on the proof layer.
You've descended nine turns of the helix. Bring a decision, an access flow, or a device from your environment — we'll seal it, and your team will verify the evidence, without PHI and without trusting us.
What is verifiable healthcare AI?
Verifiable healthcare AI is AI whose decisions carry cryptographic, independently-checkable evidence that a specific clinical-AI decision came from an approved, un-tampered model running on clean data — captured the moment the decision is made and provable later without exposing patient information. As artificial intelligence moves out of the research bench and into triage, imaging, documentation, record access, and, in early pilots, autonomous prescribing, a new class of risk appears that traditional security never addressed: not the theft of data, but the unprovable decision. A recommendation reaches a patient, a record is opened, a connected device acts — and weeks later no one can confirm which model produced the result, on which version, by whose identity, or whether the inputs were intact. Conventional audit logs cannot answer that; they can be edited, and they usually sit beside the very data they are meant to protect. RankShieldMD closes the gap by producing cryptographic, externally-anchored evidence that a clinical-AI decision came from an un-tampered model on clean data, that every record access was made by a verified identity, and that connected and implanted devices carry post-quantum-safe credentials. Two principles govern the design, and we hold to both honestly: attest, don't decide — RankShieldMD proves provenance, it never renders clinical judgments — and prove without exposing, so verification never requires revealing protected health information.
It is security and quality tooling — not a medical device — and it never sees protected health information. It attests decisions; it never makes them.
How do you verify an AI decision without exposing patient data?
By separating the proof from the data — recording verifiable statements about a decision rather than its clinical contents. RankShieldMD attests each decision in four steps, and none of them touch protected health information. First, it registers a cryptographic baseline of the approved model: a hash of the model and its container, so there is a fixed reference for what "the validated model" means. Second, at decision time, it captures digests of the inputs and the output — never the underlying PHI, which is rejected at the guard before anything is sealed. Third, it seals those digests, together with the model fingerprint and a per-decision credential, to an append-only transparency log built on the same principle as certificate transparency, signs them with composite post-quantum cryptography, and anchors the log root to an external record so the whole structure is pinned in time. Fourth, it emits an evidence package with a verify recipe: an auditor, an FDA reviewer, or opposing counsel can recompute the hash chain and confirm the signed root using standard tools, without access to your systems and without trusting RankShieldMD. Tamper with the model, the data, or the record after the fact, and verification returns false. The medical data stays where it belongs, governed by the clinical systems built for it; RankShieldMD holds only the cryptographic proof about the decision.
Why does healthcare AI need to be verifiable and quantum-safe?
Because the evidence behind a clinical decision must stay trustworthy for as long as the decision matters, and in medicine that is measured in decades. Verifiability answers the immediate question — can you prove this decision came from the approved model, or are you asking everyone to trust a dashboard? Quantum-safety answers the durable one. Implants and connected devices outlive the cryptography they ship with, and they cannot be recalled to patch it; a proof that is unforgeable now but forgeable in fifteen years is not good enough for a record that documents a person's care. There is also a specific, documented threat pattern — "harvest now, forge later" — where an adversary copies today's classically-signed evidence and waits for a capable quantum computer to forge or repudiate it retroactively. Long-retention healthcare records are an attractive target precisely because they are kept so long. That is why RankShieldMD signs every attestation with composite post-quantum signatures, pairing ML-DSA-65 with Ed25519, and rotates device keys in the field so a fleet migrates without a recall. We state the posture honestly: it is quantum-safe, not quantum-proof. No quantum computer capable of breaking current cryptography exists yet, and no one can guarantee any system is unbreakable; what we can do is build to the NIST post-quantum standards so a decade of decisions stays defensible.
How is proving a decision different from documenting risk?
They operate at different layers, and the difference is the whole point. AI governance platforms, model cards, and an AI bill of materials describe a model at the supply-chain and policy level — its components, datasets, lineage, and the controls around it. Model monitoring watches aggregate behavior over time, flagging drift or anomalies across many predictions. Both are useful, and RankShieldMD can emit AIBOM artifacts itself. But documentation and monitoring describe how a system is supposed to behave and how it behaved in aggregate; neither proves anything about an individual runtime decision. When a specific result is questioned after the fact, an inventory of the model's ingredients and a chart of last month's drift cannot tell you whether this decision came from the approved model on intact data, by a verified identity. Per-decision provenance is the finer, missing layer: a cryptographic receipt for each decision, sealed at the moment it happens, that can be verified in isolation. It is the difference between documenting that a kitchen follows food-safety policy and holding a signed, tamper-evident receipt for the specific meal that was served. Governance platforms document risk and policy; monitoring tools observe behavior; RankShieldMD proves the decision. The three are complementary — provenance is simply the one that survives the question "prove this one."
Who is RankShieldMD built for?
For the people who are accountable when AI acts inside care, and who need proof rather than assurances. Health systems and their security committees carry the burden of showing a board and an auditor that AI, device, and record-access risk is under control — RankShieldMD turns that risk into evidence they can check, without expanding their PHI footprint. Medical-device manufacturers face FDA §524B cybersecurity expectations and the reality that implants outlive their cryptography; they get signed cybersecurity artifacts and post-quantum device identity that migrates in the field, no recall required. Clinical-AI and SaMD vendors face the hardest sales question of all — "why should we trust your model?" — and RankShieldMD lets them answer with per-decision proof that the model was genuine and the data clean, while staying non-device, because it attests rather than renders. The pattern repeats across telehealth platforms proving an order was signed by the clinician it claims, and across health-information exchanges that need to show a disclosure was made by a verified actor for a stated purpose. Underneath all of them is one verifiable fabric and one honest boundary: RankShieldMD is security and quality tooling, not a medical device, and it never sees protected health information. It gives each of these buyers an independent integrity signal for the AI, access, and devices in their care, without entering the clinical pathway and without asking anyone to extend trust they cannot check. The common thread is accountability: wherever a decision, an access, or a device action would otherwise rest on "trust us," RankShieldMD replaces the assurance with a receipt anyone named above can verify for themselves.
Built for the people accountable when AI acts inside care.
Security your board can verify.
Turn AI, device, and record-access risk into evidence a board and an auditor can check — without expanding your PHI footprint.
Explore →FOR DEVICE MANUFACTURERSEvidence for every submission.
Signed §524B cybersecurity artifacts, plus post-quantum device identity that migrates in the field — no recall.
Explore →FOR CLINICAL-AI VENDORSProve your model. Keep your buyers.
Per-decision proof your model was genuine and the data clean — while staying non-device, because it attests rather than renders.
Explore →Evidence that supports FDA and HIPAA — not a device, not a certification.
Evidence for §524B
- Postmarket monitoring — signed decision-and-integrity log
- Reasonable assurance — post-quantum identity + containment
- SBOM / AIBOM — CycloneDX-conformant manifests
- Residual-risk dossiers for unpatchable devices
Stays non-device: attests decisions, never renders them (FDA CDS 4th criterion). Supports your submission — not a clearance.
PHI-free audit evidence
- Audit controls — §164.312(b) tamper-evident access records
- Accounting of disclosures — §164.528 sealed, purpose-tagged
- Access control — §164.312(a) verified actor identity
- Ahead of the proposed Security Rule updates
PHI-free by construction — digests only. Adopting it shrinks your PHI footprint. Proposed rules are proposed, not final.
What we are careful never to claim.
We didn't invent the concept
Verifiable clinical-AI audit is a published academic idea. RankShieldMD ships the commercial, externally-anchored, post-quantum, per-decision implementation — not the invention of the concept.
It supports compliance — it isn't compliance
It produces evidence that supports FDA §524B submissions and HIPAA audit requirements. It is not itself a clearance or a certification, and it never makes a medical claim.
It never sees PHI
Model, input, and output are reduced to digests; access is checked by verified identity. Raw identifiers are rejected at the guard. The ledger is useless to anyone who steals it — there's no protected data inside.
Ask RankShieldMD about healthcare AI security.
What is verifiable healthcare AI?
It is AI whose decisions come with cryptographic, independently-checkable evidence — proof that a specific clinical-AI decision came from an approved, un-tampered model on clean data. RankShieldMD produces that evidence at the moment the decision is made, so it can be verified later without trusting the vendor and without exposing patient information.
Why does every AI decision in medicine need a receipt?
Because when a decision is later questioned, someone must prove what actually happened — which model ran, on which version, on what data. Logs can be edited and dashboards ask for trust. A cryptographic receipt, sealed at decision time, makes the answer checkable instead of assertable.
Does RankShieldMD make or influence the clinical decision?
No. It attests decisions made by other systems and never renders, scores, or recommends one. That boundary is deliberate: it keeps RankShieldMD non-device under the FDA clinical-decision-support criteria, and it keeps clinical judgment with clinicians.
Is RankShieldMD a medical device?
No. It is security and quality tooling that helps hospitals, device makers, and clinical-AI vendors meet their obligations. FDA classification turns on intended use — RankShieldMD’s use is to attest that a decision, access, or device was genuine, not to make or drive one.
Does it see protected health information?
No. It is PHI-free by construction. It seals digests of the model, inputs, and output, and works on verified identities and de-identified indicators only. Raw identifiers are rejected at the guard and never enter the ledger. Adopting it shrinks your PHI footprint rather than growing it.
Can we verify the proof ourselves?
Yes — that is the whole point. Every proof ships with a verify recipe. Anyone holding the evidence package can recompute the hash chain and confirm the post-quantum-signed root using standard tools, without access to your systems and without trusting RankShieldMD.
What happens if a model is swapped, drifts, or data is poisoned?
The sealed digests will not match the approved baseline, and verification returns false — surfacing the discrepancy. Tampering with the model, the data, or the record after the fact is detectable, not silent.
What does it prove that my other security tools do not?
Three things no incumbent proves at the decision level: that a clinical-AI decision was genuine, that each record access was made by a verified identity, and that connected devices carry post-quantum-safe credentials. Device-security tools inventory and segment; governance tools document risk. RankShieldMD proves the decision.
What open standards is it built on?
The transparency log follows the certificate-transparency model (RFC 6962), signed requests use RFC 9421, signatures use the NIST post-quantum standards, and attestation aligns with the IETF RATS architecture. Built to open standards so the evidence is portable and checkable outside our systems.
Did RankShieldMD invent verifiable clinical-AI audit?
No — the concept exists in published research. RankShieldMD ships the commercial, externally-anchored, post-quantum, per-decision implementation. We are careful never to claim we invented the idea.
How does this support FDA or HIPAA obligations?
It produces the integrity evidence that FDA §524B postmarket monitoring and HIPAA audit-control requirements (§164.312(b), §164.528) rely on. It produces evidence that supports compliance — it is not itself a clearance or a certification, and it makes no medical claim.
Will this make us HIPAA or FDA compliant?
No software does. It produces verifiable evidence that supports specific requirements; compliance is your organization’s overall posture. We never claim otherwise.
Is it quantum-safe?
Yes — proofs are signed with composite ML-DSA-65 and Ed25519 so evidence stays defensible as cryptography evolves. It is quantum-safe, not quantum-proof: no quantum computer capable of breaking today’s cryptography exists yet, and we never claim otherwise.
Proof for every clinical decision.
Bring a decision, an access flow, or a device from your environment. We'll seal it — and your team will verify the evidence, without PHI and without trusting us.