A benchmark that ignores the operator’s duties can make a model look safer than the product built around it. Monitoring, documentation, escalation and change control are where clinical AI meets the real world.
Agreed. None of the 8 benchmarks we looked at test monitoring, escalation or change control. They score the model on its own, but what patients actually meet is the whole deployed system.
Eight benchmarks and not one of them looks at what happens after go-live. That's the part the hospital actually carries, so it's a strange thing to leave out of the score.
Take a look at the Pacific AI Guardian, which does precisely that - runs these test suites, both normal and red-teaming cases, continuously in production:
You can define monitoring and alerting thresholds per benchmark. Using the same suite for validation (pre-release) and monitoring (in production), coupled with test suites that have meaningful confidence intervals, also means that you can easily set when drift (for example, due to an AI vendor changing their model on the backend) becomes statistically significant versus noise.
Regulation usually arrives as a checklist, but the hard part is keeping the duty alive after deployment. Someone has to own the evidence when the model, workflow or vendor changes six months later.
Exactly. That's why we tie every test case back to the regulation it came from. When the law, the model or the vendor changes, you can see which tests need rerunning instead of finding out at the next audit.
The vendor change is the one that worries me. A new law gets everyone's attention. A quiet model update on the vendor's side can go live without anyone rerunning a single test.
A benchmark that ignores the operator’s duties can make a model look safer than the product built around it. Monitoring, documentation, escalation and change control are where clinical AI meets the real world.
Agreed. None of the 8 benchmarks we looked at test monitoring, escalation or change control. They score the model on its own, but what patients actually meet is the whole deployed system.
Eight benchmarks and not one of them looks at what happens after go-live. That's the part the hospital actually carries, so it's a strange thing to leave out of the score.
Take a look at the Pacific AI Guardian, which does precisely that - runs these test suites, both normal and red-teaming cases, continuously in production:
https://pacific.ai/guardian/
You can define monitoring and alerting thresholds per benchmark. Using the same suite for validation (pre-release) and monitoring (in production), coupled with test suites that have meaningful confidence intervals, also means that you can easily set when drift (for example, due to an AI vendor changing their model on the backend) becomes statistically significant versus noise.
Regulation usually arrives as a checklist, but the hard part is keeping the duty alive after deployment. Someone has to own the evidence when the model, workflow or vendor changes six months later.
Exactly. That's why we tie every test case back to the regulation it came from. When the law, the model or the vendor changes, you can see which tests need rerunning instead of finding out at the next audit.
The vendor change is the one that worries me. A new law gets everyone's attention. A quiet model update on the vendor's side can go live without anyone rerunning a single test.