Dr. Ami Bhatt's team at the American College of Cardiology found that most FDA-approved cardiovascular AI tools sit unused within three years. The barrier isn't regulatory approval or technical accuracy. It's implementation infrastructure. Without deployment workflows, communication campaigns, and technical integration planning, even validated tools fail at scale.

Bhatt distinguishes "collaborative intelligence" from "augmented intelligence" because collaboration acknowledges that physicians must co-design algorithms, determine deployment contexts, and iterate on outputs that won't be 100% correct. Augmentation falsely suggests AI works flawlessly out of the box, setting unrealistic expectations that kill adoption when tools underperform in production.

Her risk stratification approach prioritizes low-risk patients with high population impact over complex diagnostics. Newly diagnosed hypertension patients (affecting 1 in 2 people, 60% undiagnosed) are clinically low-risk today but drive massive long-term costs if untreated. These populations deliver better ROI than edge cases but require moving from episodic hospital care to continuous monitoring infrastructure that most health systems lack.

Topics discussed:

Risk stratification methodology prioritizing low-risk, high-impact patient populations
Infrastructure gaps between FDA approval and scaled deployment
Real-world evidence approaches for AI validation in lower-risk categories
Synthetic data sets from cardiovascular registries for external company testing
Administrative workflow automation through voice-to-text and prior authorization tools
Apple Watch data integration protocols solving wearable ingestion problems
Three-part startup evaluation: domain expertise, technical iteration capacity, implementation planning
Real-time triage systems reordering diagnostic queues by urgency