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clinical decision.
RankShieldMD is a verifiable AI and post-quantum security layer for healthcare. It gives your practice a cryptographic, independently-checkable receipt for every clinical-AI decision, record access, and telehealth order. Proof you can verify yourself, without ever exposing protected health information, and without being a medical device.
Why does every AI decision in medicine need a receipt?
AI now drafts your notes, flags your images, and routes your intake. When a decision is questioned months later, logs can be edited and dashboards ask for trust. A receipt makes the answer checkable instead of assertable. Small practices carry the same accountability as large systems, with far less to defend it.
The AI scribe note is disputed
A patient contests what an ambient-AI scribe wrote. Was it the model you approved, on the real encounter, or a drifted version? RankShieldMD sealed a receipt the moment the note was generated.
recompute · model matches ✓A telehealth order is challenged
An auditor asks you to prove the ordering clinician actually signed it, and that no one tampered with it in transit. The signature is verified against their enrolled identity, with no PHI required.
tampered order · signature fails ✗Prove the decision. The access. The encounter.
Four things no dashboard can prove. Each is sealed to one verifiable record, each is checkable on its own, and none of it touches protected health information.
Prove the decision
A cryptographic receipt that a clinical-AI decision came from the approved model on clean data.
sealed · verifiableProve the access
PHI-free, tamper-evident evidence of who opened which record, and why.
HIPAA 164.312(b)Prove the encounter
Signed clinical orders and anti-deepfake checks on telehealth consults.
signature verifiedVerify it yourself
Every proof recomputes with standard tools, without access to your systems, and without trusting us.
independent checkBuilt for small and mid-sized medical teams adopting AI.
Practices & specialty clinics
Using ambient scribes, imaging AI, or intake bots across a handful of providers.
Imaging & diagnostic centers
Running AI detection or triage on scans, defending the read and the model.
Telehealth & digital health
Proving the clinician signed the order and defending against deepfake consults.
Clinical-AI & health-tech vendors
Small teams whose hospital buyers keep asking, why should we trust your model?
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.
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. Governance platforms document risk and policy; monitoring tools observe behavior; RankShieldMD proves the decision.
Run the check yourself.
Anyone can claim their clinical AI is safe. RankShieldMD lets you check. Each decision becomes a receipt you can recompute independently: the model fingerprint, the data digest, and the post-quantum signature over an append-only record. Tamper with any of it, and verification returns false.
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. There is a 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. 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 we build to the NIST post-quantum standards so a decade of decisions stays defensible.
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 and 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 and 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.
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, so adopting it shrinks your PHI footprint.
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 makes no medical 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.
Ask the founder.
Straight answers about verifiable healthcare AI. Tap a question, or type your own.
- 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 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 practices, imaging centers, telehealth companies, and clinical-AI vendors meet their obligations. FDA classification turns on intended use, and 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, so 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.
- 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.
Put your medicine on the proof layer.
Bring a decision, an access flow, or a telehealth order from your environment. We'll seal it, and your team will verify the evidence, without PHI, and without trusting us.