AI-powered The standard for conducting cancer research
Plenty of tools hand you results. Only IO hands you the code too.
Spring 2026 pilot · Priority for leading cancer centers · No fees
Three walls you hitin every cancer study.
The problems cited most often in 1-on-1 interviews with 300 senior clinicians and researchers from ASCO · ESMO · GBCC.
Takes too long (speed)
The research question is already in your head — but turning it into results takes forever.
Demands too much expertise (breadth)
Clinical knowledge, statistics (survival, competing risk), and programming (R/Python) are all needed at once.
Results appear, but can't be verified (black box)
You get the figures and p-values, but often can't trace how they were produced.
Medical data is already abundant and still growing. But turning it into trustworthy research is still manual and impossible to verify.
Same study. Same rigor.Fourteen weeks becomes six days.
NSCLC EGFR+ retrospective comparative effectiveness · same cohort · n=12,847
- 010.5 wkFrame the research questionPI
- 024–6 wkIRB / data-use submissionReg ops
- 032–3 wkExtract from warehouseData eng
- 042 wkHarmonize codes (ICD / NLP)Curator
- 051–2 wkBuild cohort, version by handAnalyst
- 061 wkRun statistical modelsBiostats
- 071 wkQC · sensitivity · peer checkPI + 2nd
- 081–2 wkDraft · audit · sign-offPI
- 010.5 dFrame the research questionPI
- 02< 1 mCompliance & PHI gateGOV agent
- 036 mFederated query planQP agent
- 0414 mCode-set mappingCB agent · HITL
- 058 mCohort construction · v4CB agent
- 0622 mAdjusted analysis (IPTW + CI)ST agent
- 0711 mQC · replication · repro scoreQC agent
- 082 hSign & lock · manuscriptPI · HITL
From question to result,one continuous flow.
Home, literature review, cohort building, and analysis — every stage runs in one connected workspace, with you approving each step.
Start from the research question
Drop a question and pick a mode — review literature, design a study, build a cohort, or run the full pipeline. Your notes and sources stay in one place.
Cited evidence, not vibes
The agent surfaces PubMed papers inline with PMIDs you can inspect and add to sources in one click — every claim stays traceable.
Reproducible cohorts, by construction
Datasets and PubMed refs become a versioned cohort with a CONSORT-style flow diagram and exported tables — no manual SQL.
Adjusted analysis you can re-run
Survival, IPTW, and protein-expression models produce figures, code, and a reproducibility score — open the script and reproduce any result.
Frequently asked
IO is not a chatbot — it is an operating system for doing research. Instead of a chat bubble, it runs on a workflow: query, cohort, analysis, validation, sign. Every step ships a pinned, version-locked output.
Most tools return only figures and p-values. IO ships the Python code, data mappings, execution log, and HITL approvals that produced the result. One Reproduce click rebuilds the same result anywhere.
Research data never leaves the site. Every analysis runs in an isolated sandbox, and only the code-set registry is shared across sites. Queries that risk PHI exposure are blocked before they run.
Ask your research question in plain language and IO decides the workflow and writes the code. But all code is fully exposed, so PIs fluent in statistics and programming can review, edit, and re-run it directly.
Data lineage, code versioning, HITL coverage, and external replication — four axes scored 0–100 and combined. Every output is signed and locked with content-addressed lineage.
The Spring 2026 pilot prioritizes PIs at NCI-designated and Korean academic cancer centers. Apply with a work email and we will reach out in order.