CADUCEUS — Virtual Molecular Tumor Board
Hand it a full oncology case and 55+ specialist agents across 7 layers convene a molecular tumor board — diagnosis, therapy, pharmacy, trials, evidence, dissent — and return a fully-cited, guideline-concordant recommendation with a human on the final gate.
The problem
A molecular tumor board is the multidisciplinary review every cancer centre runs weekly: pathology, radiology, genomics, labs and notes fused into one defensible plan. It is the textbook case for multi-agent AI — and the textbook case for getting AI in medicine wrong.
The three things that sink clinical AI are exactly what this is built around: no provenance (nothing is trustable), silent consensus (no one argued the other side), and automating a decision that must legally and ethically keep a human on the hook.
Architecture
- 1
Ingestion & medallion
Bronze raw -> Silver FHIR-normalised -> Gold unified Patient Case object the agents consume. Synthetic Synthea-style cases, or a real de-identified TCGA patient assembled live from the NCI GDC open tier.
- 2
Knowledge layer
Hybrid retrieval: a vector view over literature / drug labels / guideline text and a knowledge graph of gene -> drug -> disease -> interaction relationships, every chunk carrying a source id.
- 3
55+ agents, 7 layers (LangGraph-style)
Hierarchical supervisor (Board Chair) over departmental sub-graphs: Intake, Diagnostic (Pathology / Radiology / Molecular / Labs), Therapeutic + Pharmacy, Evidence / Trials / Guidelines, Supportive, and Synthesis.
- 4
Genuine concurrency
The four diagnostic departments run as one parallel wave; Variant Actionability, Patient-Trial Matcher and Abstract Screening fan out one worker per item (map-reduce) — peak concurrency ~150, streamed live to the UI over SSE.
- 5
Synthesis & safety
A Consensus Synthesizer fuses modalities; a Dissent / Red-Team agent argues against the recommendation; a Contradiction Checker and a Citation Validator block unsourced or conflicting claims before a human sees them.
- 6
Live data path
On real patients: variants annotated against CIViC, drug toxicity from openFDA + FAERS, trials from ClinicalTrials.gov v2, literature from Europe PMC — all keyless free public APIs, with honest open-tier data gaps marked, never fabricated.
Key tradeoffs
Provenance or it did not happen — a Citation Validator rejects any recommendation lacking a retrievable source.
Why · In oncology a confident-but-wrong claim is a liability. No source, no statement; 100% of claims trace to a citation.
A dedicated Dissent / Red-Team agent exists solely to attack the consensus.
Why · Silent agreement is the failure mode of committee AI. The minority view and modality conflicts are surfaced, not smoothed over.
Decision support, not decision making — human-in-the-loop checkpoints after diagnosis and before finalisation.
Why · Clinical AI that automates the decision is what regulators and clinicians reject. CADUCEUS proposes; a clinician disposes.
Keyless and free — no LLM API key, no credentialed data; real patients come from the TCGA open tier.
Why · A pipeline anyone can run on real public data beats a demo on invented data behind a paywall. Built only on free / synthetic / de-identified sources.
Eval results
Every clinical claim carries a retrievable source id, verified by the Provenance / Citation Validator across full runs; unsourced claims are blocked from output.
Inter-department parallelism plus per-variant / per-trial / per-abstract map-reduce fan-out; measured at the heaviest screening window.
Synthetic cases scored against NCI PDQ for matched cancer types; on live TCGA cases this is shown honestly as evidence-grade strength, since PDQ has no machine-readable API.
GDC / TCGA, CIViC, openFDA + FAERS, ClinicalTrials.gov v2, Europe PMC and RxNorm — all reachable with no key, verified live.
Production proof
The artifact that keeps the numbers honest — the eval harness / monitoring gates that run in CI, not a one-off notebook result.
Provenance + dissent + human gates by construction
CI · passingCADUCEUS drafts; clinicians decide. Every recommendation has been argued against and every line traces to a public source. A research prototype, not an FDA-cleared medical device.
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