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The end of benchmarks.
The beginning of certification.

Media resources on the first quantitative safety standard for clinical AI, the Gomola Framework, and the DeepSensi Cognitive Medical OS. For interviews, data, and auditor access to research protocols: [email protected].

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The benchmark, in context

The hardest cases in medicine. Ranked.

System — exact diagnosis on NEJM CPCAccuracy
DeepSensi — Cognitive Medical OS
86.0%top-1 · 93.7% top-3
Microsoft MAI-DxO (o3)
85.5%
OpenAI o3
78.6%
OpenAI GPT-4o
49.3%
OpenAI GPT-4
39%
Practicing physicians (unaided)
19.9%

Others guess the answer. DeepSensi deduces it — a patent-pending, SIL-4-grade anti-hallucination architecture, auditable to every step. A score you cannot audit is still a guess.

All figures on the publicly available NEJM Clinicopathological Conference (CPC) cases — the same benchmark used across the field. DeepSensi — 301 cases (2014–2023), development-cohort; 93.7% in its top 3. MAI-DxO, o3, GPT-4o, physicians — SDBench, 304 cases (Nori et al. 2025). GPT-4 — Kanjee et al. 2023.

What only DeepSensi brings

The proof the number can't show.

CapabilityDeepSensiFrontier LLMs*Orchestrated diagnostic AIAmbient scribe
SIL-4-grade anti-hallucination architecture3.23×10⁻⁶
Open safety-certification standard (DSS)
Deterministic multi-layer verificationpartial
Verified deductive chain — auditable, not a guess
Structured "I don't know" (LIMBO)
Anti-bias adversarial design
Court-grade audit + liability splitpartial
Physician score + outcome-gated royalties (GCPS)
NEJM-CPC exact-diagnosis accuracy86.0%39–79%80–85.5%n/a

*Frontier LLMs = general-purpose and medical models from OpenAI, Anthropic, Google, xAI, DeepSeek and others. Categories, not a rating of any single product; every "✗" means "none published as of July 2026." DeepSensi's anti-hallucination architecture is patent-pending, with a measured, SIL-4-grade safety bound established by fault-tree analysis (IEC 61025); the DSS certificate reads two-dimensionally — Platinum architecture · certified worst-case bound 3.23 × 10⁻⁶.

A panel with no ego

The human errors that vanish.

No fatigue, no hierarchy, no memory of a "difficult patient" — and a mandatory adversary in every case. DeepSensi's architecture is designed to eliminate the classic human diagnostic biases, and to engineer against machine bias through cross-vendor independence and deterministic evidence checks.

Anchoringfirst impression sticks
Confirmationseeks agreeing evidence
Premature closurestops reasoning too early
Availabilityrecent cases over-weighted
Base-rate neglectignores prevalence
Search satisficingmisses the second diagnosis
Framingpresentation sways judgment
Ascertainment / stereotypingage, sex, origin
Overconfidencecertainty without basis
Authority gradientjunior won't challenge senior
Groupthinkpanel follows the loudest voice
Fatigue & loadthe human factor, absent by design

Read & download

Feature · English

The End of Benchmarks. The Beginning of Certification.

How DeepSensi and the Gomola Framework move medical AI into the world of safety norms — anti-fragility, LIMBO, the 4.6%→0.0% result, and the full cognitive infrastructure now revealed.

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Press release · English

DeepSensi publishes the first quantitative safety standard for clinical AI — free to the industry

The announcement: six papers to medRxiv and NEJM AI, the quantified SIL-4-grade bound, 93.7% top-3 with a 0.0% missed-critical rate, and the open, royalty-free standard.

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Feature · English

The Medicine That Remembers You

The patient-facing machinery: a digital twin that never stops watching, retroactive alerts that arrive years after a visit, Active Sensing, plain-language reports — and an epidemic radar built from ordinary doctor's notes.

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Feature · English

The First Physicians Who Earn Royalties for Being Right

Inside the physician's "Medical Exosuit": a consilium that argues back, documentation that writes itself, referrals with built-in anti-kickback compliance — and royalties released only when patient outcomes confirm the insight.

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Feature · English

An Epidemic Radar Built from Ordinary Doctor's Notes

How DeepSensi turns the de-identified exhaust of everyday documentation into a privacy-preserving, always-on early-warning system for the next outbreak — offered to public health as a royalty-free public good.

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Feature · English

The Medical Exosuit Nobody Saw Coming

Why "medical AI" undersells what was built: every physician puts on an exosuit with the power of a teaching hospital — and the real category is an operating system for medicine, defined by an open standard.

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Press kit

Boilerplate, fact sheet, quotes, logos & FAQ

Canonical numbers, approved quotes, founder bio, an honest “what we do not claim” box, brand assets, and the full downloadable bundle. SIL-4-grade bound (3.23×10⁻⁶ per assertion); NEJM CPC N=301 — 86.0% top-1, 93.7% top-3, 0.0% missed-critical, 14.3 s median.

Open the press kit →

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