Three tools and a research library, one governance philosophy. Pick a tool to see what it does and how to engage with it.
AI Governor v1 evaluates clinical AI predictions against deterministic clinical scoring tools published in medical literature. KDIGO for AKI. Padua for VTE risk. C-SSRS for psychiatric assessment. APACHE II for ICU mortality. Each evaluation is cryptographically signed with Ed25519. Click through to see what each modality looks like.
Governance and audit infrastructure for clinical AI vendors. Not a clinical-decision-support device — not used on real patients. Inputs are synthetic, public-dataset, or de-identified.
Discordant chest x-ray case: AI device says "normal", human radiologist reads right lower lobe consolidation, early pneumonia. The pipeline runs imaging-modality probe → body-region specialist → 3-variant adversarial vision reasoning, returns a procurement-readable verdict with named verdict-provenance (primary · text_override · scope_gap_kept) and a methodology-audit grounding signal (grounded · partial · ungrounded · no_clinical_claim) listing which named criteria sets the reasoning referenced. ~45 s end-to-end.
Adam will show you the methodology, the verification flow, and what a sandbox evaluation looks like at your institution.
Start 30-day Trial View Pricing Contact AdamThe Discovery Engine is the research arm. It surfaces contradictions and patterns across published clinical mortality data. Two engagement paths: licensed engine in your environment with monthly contradiction reports, or single-paper integrity scoring.
Cross-dataset replication for mortality, sepsis, and risk-stratification claims. Pre-registered methodology, OSF evidence chain.
Ed25519-signed evidence hashes for every finding. Public methodology. Confirmatory replication is pre-registered before clinical claims are made.
Dedicated engine instance, your data stays in your environment, monthly contradiction reports. Or single-paper Evidence Integrity Score on request.
Whether it is a license, a single-paper integrity scan, or a partnership conversation, Adam responds personally within 24 hours.
Start 30-day Trial View Pricing Contact AdamCaliper takes a drug-condition-population hypothesis and runs it through a 6-tier validation framework on credentialed ICU cohorts. Propensity-score matching with 40 covariates. Cross-dataset replication on MIMIC-IV 3.1 and eICU-CRD 2.0. COVID-era stratification. Every finding is signed with Ed25519 and externally verifiable via the public /keys/ page. Findings that don't survive PSM are reclassified to CONFOUNDED_INVERTED and surfaced in the corpus, not hidden. The framework catches its own errors.
Engine runs cohort cuts, PSM, era stratification, and signs the result.
ROBUST_PUBLISHABLE → ROBUST → CANDIDATE → HYPOTHESIS_GENERATING → CONFOUNDED_INVERTED → PANDEMIC_ARTIFACT.
Ed25519 signature, public verify URL, no authentication required to validate.
Encrypted at rest, scoped to your session, never crosses to the credentialed cohort path.
40 covariates, 1:1 nearest-neighbor matching, caliper 0.2·SD, era stratification all surfaced per finding.
MIMIC-IV 3.1 (94,458 ICU stays per PhysioNet release; Caliper filters to a credentialed adult subset) and eICU-CRD 2.0 (200,859 unit stays across 208 hospitals). Patient-level data never leaves the credentialed path.
Targets: real-world evidence teams, pharmacoepidemiology groups, signal-detection scientists, clinical-pharmacology directors, biotech R&D teams. 50 hypothesis validations per day, 500 per month, up to 5 team accounts under one organization. Every result cryptographically signed.
Start 30-day Trial View Pricing Contact AdamEvery paper here is approved by Adam personally. No auto-indexing. Each preprint is shared for pre-submission peer commentary and carries the full evidence chain — datasets, code, OSF preregistration, signed hashes.
Across 286,510 ICU encounters in MIMIC-IV and eICU-CRD, mainstream sepsis prediction benchmarks correlate with hospital billing patterns rather than biological sepsis criteria.
Read the full paper →Sepsis-AI tools trained on academic-medical-center data systematically misfire in community hospitals. The miscalibration is structural, not statistical, and pre-registered replication confirms it.
Read the full paper →Published mortality estimates for elderly ICU patients underestimate true risk by 66–168% across MI, sepsis, and respiratory-failure cohorts. Cross-validated on 201,905 encounters.
Read the full paper →The full library, including abstracts, OSF links, and download bundles, lives at /research.
Tell Adam which paper or which question, and he'll reply directly.