Galen is an autonomous AI that does causal cancer research 24 hours a day — tracing mechanisms, generating hypotheses, and grounding every claim in experimental evidence.
What we’re building toward is cancer superintelligence — and making it available to every patient, clinician, and researcher who needs it.
More than 5,000 cancer studies are published every week. But the challenge facing patients and clinicians isn’t finding this information — it’s integrating across dimensions that don’t naturally connect. A patient’s tumor isn’t defined by one mutation, one pathway, or one treatment. It’s shaped by the causal interactions between all of them — interactions that evolve as resistance develops, as new drugs are introduced, and as the tumor adapts. That demands more than search. It demands reasoning that traces causes, models interventions, and anticipates what happens next.
Galen continuously integrates experimental evidence from the world’s biomedical databases — genomic studies, clinical trials, drug mechanisms — into a causal reasoning engine. Most AI finds correlations in data. Galen traces the mechanisms underneath — why a mutation drives resistance, why a treatment works, and why it might stop working.
Most AI in cancer works at the first level — association. Drug X is associated with mutation Y. That’s a lookup table, not an understanding.
Galen reasons at three levels. Association: osimertinib is commonly used for EGFR L858R. Intervention: inhibiting EGFR suppresses downstream MAPK signaling, slowing tumor growth. Counterfactual: if this patient carried a different mutation — say, C797S — a different mechanism of resistance would emerge, demanding a different therapeutic strategy.
The difference matters most when the evidence is thin — rare mutations, unusual combinations, newly approved therapies where historical data doesn’t exist yet. That’s where pattern-matching breaks down and causal reasoning becomes essential.
Every answer traces back to its source. Choose a question and see the evidence chain.
Choose a question:
The same engine powers every product — so every question asked makes the intelligence better for everyone.
For patients & caregivers
Care Navigator
Understand your diagnosis, explore your options, and ask the questions that matter — with every answer grounded in evidence.
Open Care Navigator →For clinicians
Tumor Board
Deeper case preparation in less time — evidence synthesis that fits the way you already work.
Learn more →For developers & researchers
Developer API
Cancer intelligence as an API — integrate causal reasoning and the world’s biomedical evidence into what you’re building.
Open API docs →Galen maintains a structural causal model of cancer biology. It doesn’t just report that a drug works — it traces why: from mutation, to mechanism, to treatment, to resistance. That causal chain is what allows Galen to reason about cases it has never seen before.
Association — “Drug X works for mutation Y.”
Intervention — “Inhibiting target Z suppresses this pathway.”
Counterfactual — “A different mutation would change the strategy.”
Built with care for sensitive health data. Your questions are yours.
Physicians, engineers, and researchers — not a general-purpose AI team chasing healthcare as a vertical.
1,994,982 entities · 9,918,844 relationships · Peer-reviewed databases
Whether you’re navigating a diagnosis, preparing for a case, or building something new — Galen is ready.