Clinical-Grade Dataset Reliability
A concise executive whitepaper explaining why reliability infrastructure is becoming mandatory for biomedical AI deployment and what evidence enterprises expect before approving production use.
What’s inside
- Why “accuracy” doesn’t equal reliability
- Deployment failure modes (drift, edge cases, subgroup gaps)
- What procurement and clinical governance actually require
- The Trinzz reliability workflow: audit → certification → monitoring
- Checklists: evidence package, release criteria, PSI thresholds
Best next step
If you’re deploying clinical AI, start with an audit. You’ll get a scorecard, traceability gaps, and a plan to reach certification-ready releases.