Hospitals are racing to adopt AI. More than 2,000 clinical AI tools hit the U.S. market last year - from ambient scribes to imaging models. But new regulations (HTI-1, Colorado AI Act, California SB 3030, White House AI Action Plan) require auditable proof that these models are safe, fair, and continuously monitored.
The problem is, most hospital IT teams can’t keep up. They can’t vet every vendor, run stress tests, and monitor models 24/7. As a result, promising tools die in pilot hell while risk exposure grows.
We saw this firsthand while deploying AI at Columbia University Irving Medical Center, so we built Parachute. Columbia is now using it to track live AI models in production.
How it works: First, Parachute evaluates vendors against a hospital’s clinical needs and flags compliance and security risks before a pilot even begins. Next, we run automated benchmarking and red-teaming to stress test each model and uncover risks like hallucinations, bias, or safety gaps.
Once a model is deployed, Parachute continuously monitors its accuracy, drift, bias, and uptime, sending alerts the moment thresholds are breached. Finally, every approval, test, and runtime change is sealed into an immutable audit trail that hospitals can hand directly to regulators and auditors.
We’d love to hear from anyone with hospital experience who has an interest in deploying AI safely. We look forward to your comments!
by Andrey
Azimov