Buyer’s guide
Everyone in this market claims to be the best. Almost nobody defines the measure. This page does: five questions that separate a clinical system from a demo — the same five any hospital, payer, or ministry can put to any vendor, including us. Ask them, and the field sorts itself.
Why “best” goes wrong
Leaderboard scores are earned on static exam questions — clean vignettes, one right answer, no consequences. Deployed medicine is interrupted, ambiguous, and liable. A system that wins the exam can still fail the ward; the yardstick itself is flawed. That argument, with data, is our paper WP-002: The Flawed Yardstick.
A model that is 95% accurate on average and silently wrong about one aortic dissection is not a 95% system — it is a liability. The number that matters is not mean accuracy but the worst-case rate of a confidently asserted falsehood, and almost no vendor will quote one.
A demo answers questions. A system carries liability: it signs into your EHR, writes back under a physician’s signature, survives your network failing, and produces an audit trail a court can use. The distance between those two is where procurement decisions die — usually a year after the pilot.
The measure
Vendor-agnostic by construction. Score anyone with them — including us.
| Question | A demo offers | A system shows |
|---|---|---|
| 1 · Is the validation prospective? | Retrospective benchmarks, self-graded | A pre-registered, silent-mode study on live cases with independent adjudication and locked endpoints |
| 2 · Is safety a number? | “State-of-the-art accuracy” | A formally derived worst-case bound per assertion, with the derivation published and auditable |
| 3 · Is the integration real? | A chat window beside the EHR | FHIR R4 read and digitally signed write-back, SMART sign-in, on-premises and air-gapped options |
| 4 · Is the trail court-grade? | Logs the operator can edit | A cryptographically signed reasoning ledger, tamper-evident even against the operator |
| 5 · Is there a regulatory path? | “Compliance-ready” | A named SaMD posture, engagement with the regulator, and architecture mapped to the EU AI Act |
Our answers
Doctrine: we publish the proof, the standard, and the audit. We do not publish the blueprint.
Purchasing checklist
The market is flooded with transcription tools claiming to be clinical AI. Before integrating any system into your workflow, ask the vendor these four questions.
What happens when the microphone misses a critical word? Standard LLM-based scribes are probabilistic—they predict the most likely next word, meaning they will confidently invent a clinical finding rather than admit they missed it. DeepSensi is deterministic under uncertainty. The LIMBO protocol forces the system to declare 'I don't know' and flag the missing variable. Ask the vendor for their formally derived worst-case bound on confidently asserted falsehoods. If they answer with an 'average accuracy score', they do not have a safety bound.
Do you store patient data, and is privacy guaranteed by architecture or just by policy? A signed BAA is a legal shield for the vendor, not a technical safeguard for your patients. DeepSensi operates on a Zero-PHI architecture. Personal identifiers are stripped at the edge before any reasoning occurs. We do not need your patients' names to diagnose them. Ensure your vendor enforces privacy mathematically in their code, not just in their terms of service.
Does your system just take notes, or does it actively defend my license? An AI scribe that merely transcribes audio is a missed opportunity. DeepSensi’s ACS Scribe listens to the encounter and simultaneously runs a real-time, 37-specialist Differential Diagnosis (DDx) in the background. It doesn't just write the SOAP note; it highlights the one critical diagnosis you might be missing and calculates a SIL-4-grade confidence score, acting as an active clinical safety net.
If the AI makes an error, who takes the blame? Today, the answer is always the physician. DeepSensi alters this reality by providing a court-grade, cryptographically sealed evidentiary ledger for every single assertion the system makes. You can point to the exact node, rule, or piece of evidence that led to a conclusion. Ask your scribe vendor if their system provides a tamper-evident audit trail that you could take to court.
Questions