AI now helps doctors read X-rays, CTs, and MRIs. But once it's deployed in a hospital, almost no one can tell if it's still accurate. Lattice Health watches deployed imaging AI and flags it the moment it starts to slip. Congrats on the launch, Christine Park!

@sparkcpark impressive, congrats @sparkcpark

@sparkcpark Spot on. Monitoring deployed AI is critical. At Laniaka, we’re not just flagging errors—we’re building the Zero Trust infrastructure that verifies every interaction in real-time. The world can't wait for reactive fixes. We are building the future of secure communication. 🚀 #Lani

@sparkcpark 噁，这东西到底准不准啊

Huge congratulations, @sparkcpark! 🚀 Loved creating this launch video and helping tell the Lattice story. Healthcare organizations don't need more AI models rather they need more confidence in the models they're already using. Excited to see Lattice Health ( giving hospitals the visibility and governance needed to deploy AI responsibly at scale.

@sparkcpark This is the part every AI deployment eventually learns: the launch is easy, the monitoring loop is the product.

Monitoring drift matters, and here's why it connects directly to liability exposure most hospital legal teams haven't mapped yet. A chest X-ray AI approved by the FDA at 94% sensitivity doesn't stay at 94% after deployment. Patient mix shifts, scanner upgrades, workflow changes, all of it moves that number without anyone logging it. The hospital keeps using the tool. The radiologist keeps signing off. Nobody flags the gap. What Lattice is solving for operationally is the same gap I've been tracking from the legal side. Malpractice attorneys building "failure to use AI" cases, and they are building them, need two things to work. First, proof the tool was approved and available. Second, proof the tool was performing at the level cited in the literature when the harm occurred. Post-deployment drift breaks that second proof, but only if someone caught it. If no one monitored accuracy after go-live, the hospital can't prove the tool was still worth trusting, and can't prove the doctor was right to trust it. So the monitoring gap goes both ways. It weakens the argument for AI adoption as standard of care, and it removes the defense when something goes wrong with AI that was already in use. I wrote about the legal structure building around AI diagnostic performance, specifically how FDA approval starts shifting tools from optional to expected. Unmonitored drift is the hole in that whole argument.

@sparkcpark This is the exact kind of model monitoring infra that should be table stakes for any clinical deployment. The silent drift problem in diagnostic AI is a patient safety liability, and passive validation doesn't cut it.

@sparkcpark we got questions about lattice health. can you write in telegram or here in dm?

@sparkcpark monitoring ai after deployment is just as important as building it

@sparkcpark 🔥🔥