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The DeepSensi Standard · DSS

Aviation has DO-178C.
Nuclear has IEC 61508.
Medicine now has DSS.

An aircraft engine must demonstrate a failure rate below 10⁻⁹ per flight hour before it enters service. A clinical AI can currently be deployed in a hospital with no quantitative reliability requirement at all. The DeepSensi Standard closes that gap: the first certification framework that assigns clinical AI a measurable, auditable hallucination-probability bound — assessed with the same Fault Tree methodology the safety-critical industries have used for half a century.

Five pillars — each independently auditable

ICognitive DiversityNo single perspective decides. Independent specialist agents deliberate adversarially — the computational consilium.
IIEvidence IntegrityEvery source screened for provenance, retraction, funding bias, temporal decay, and population match.
IIIDeterministic SafetyDrug interactions, contradictions, and plausibility checked by pure rules and cross-vendor verification — never AI judgment alone.
IVTransparent UncertaintyA formalized "I don't know": the disagreement, a working hypothesis, and the tests that resolve it.
VImmutable AccountabilityEvery decision attributed — AI, physician, or rule — and cryptographically sealed. Tamper-proof, even against the operator.

Four levels — explicit bounds

LevelHallucination boundReference point¹Intended deployment
DSS Bronze< 10⁻³SIL 3 targetHealth information, wellness, screening
DSS Silver< 10⁻⁵SIL 4 targetPrimary-care decision support, telemedicine
DSS Gold< 10⁻⁷≈ DAL BHospitals, specialist clinics, regulatory submissions
DSS Platinum< 10⁻⁹≈ DAL ANational health systems, military, sovereign AI

¹ IEC 61508 demand-mode numerical targets; the SIL scale saturates above Silver, so comparison continues via DO-178C Design Assurance Levels (per-flight-hour — reference points, not equivalences). Bronze and Silver may be self-assessed; Gold and Platinum require third-party audit, continuous barrier telemetry, and 24-month recertification.

How certification works

An applicant documents its verification barriers and their independence, assigns per-barrier failure probabilities with supporting evidence, computes the system-level bound via Fault Tree Analysis with a justified common-cause β-factor, and multiplies by worst-case degradation. The worst-case bound determines the certified level. The methodology is fully specified in DSS-001; the companion mathematics in WP-001.

DeepSensi's own platform implements the complete Platinum architecture, with a conservatively certified bound of 3.23 × 10⁻⁶ worst-case. We publish that distinction deliberately — a standard that flatters its own author is not a standard.

"A safety standard behind a paywall is not a standard. It is a product."

— Open and royalty-free. For the entire industry, including our competitors. The sole requirement is attribution. Privacy

For hospitals: require a DSS level in your next AI procurement. For insurers: price coverage against a certified bound instead of excluding AI outright. For regulators: the framework maps directly onto EU AI Act Articles 9, 10, 14, 15, and 17. For vendors: certify against it — freely.

Read DSS-001 The science →
Zero PII· HIPAA· GDPR· EU AI Act — architected· FDA — Q-Sub engaged Verified Clinical Intelligence