Medical AI assurance infrastructure
Know where medical AI may fail.
Meditechon helps imaging AI teams evaluate data quality, generalizability, subgroup performance, and failure modes before deployment—and monitor what changes afterward.
Technical evidence assessment—not clinical diagnosis or a claim of regulatory approval.
Evidence beyond a single accuracy score
What to expect
Early-stage, honest, and evidence-led.
Meditechon is building with researchers and medical AI teams, beginning where its technical depth is strongest: CT, DICOM-RT, radiomics, and oncology imaging.
No inflated claims
Clear separation between technical assessment, clinical validation, and regulatory review.
Inspect the conditions
Evaluation considers scanners, protocols, cohorts, preprocessing, and other sources of domain shift.
Protect the data boundary
A product path designed for customer-controlled environments rather than unnecessary centralization.
One assurance workflow
From imaging conditions to actionable evidence.
A model score is only the beginning. Meditechon examines the technical chain around that score, then turns the findings into a prioritized investigation plan.
Validation Readiness Report
A consistent view of evidence readiness across data quality, reproducibility, generalizability, subgroup robustness, leakage risk, and domain shift.
Illustrative report structure; scores shown are not customer results.
Data readiness
Inspect DICOM metadata, geometry, protocols, dataset composition, RTSTRUCT relationships, and RTDOSE linkage before validation begins.
Local model evaluation
Measure performance, calibration, subgroup robustness, leakage risk, and generalizability on the population you intend to serve.
Failure analysis
Trace performance changes across scanners, protocols, institutions, acquisition parameters, anatomy, and patient cohorts.
Continuous monitoring
Track input shift, model versions, subgroup drift, and performance signals after deployment, with human review built into the workflow.
Pilot pricing
Start with the evidence question in front of you.
These are early pricing hypotheses for technical evaluation. Scope, modality, dataset size, and deployment requirements are confirmed before work begins.
Readiness Audit
A focused entry point for researchers and early AI teams.
$99one time
- De-identified dataset review
- DICOM and metadata QA
- Leakage and preprocessing checks
- Standardized readiness report
Developer
Repeatable evidence workflows for a growing medical AI team.
$299per month
- Recurring dataset evaluations
- Model-output comparison
- Subgroup and domain-shift analysis
- Exportable evidence reports
- Technical review session
Assurance
For vendors and care organizations preparing real deployments.
Customannual
- Customer-controlled deployment
- Version and drift monitoring
- Custom evaluation protocols
- Integrations and onboarding
- Dedicated assurance support
Practical questions
Clear boundaries build better assurance.
Build the evidence before deployment
Find the hidden risks in your imaging AI workflow.
Bring a de-identified dataset, model outputs, and the question your team needs to answer. Meditechon will help shape a focused technical evaluation.