A landmark review warns that US hospitals are deploying AI faster than regulators can keep up, creating a widening gap between authorization timelines and real-world use. The report highlights that many AI systems are entering clinical workflows without the level of post-market evidence and monitoring that clinical teams need to manage drift, calibration, and performance across patient subgroups. The issue is operational as much as technical: clinicians can only act on what the system reliably does for their specific population and setting. The review frames the regulatory lag as a safety and governance problem rather than a research-only concern. For biotech and digital-health companies, the practical takeaway is to plan for lifecycle evidence generation—external validation, robustness testing, and documented monitoring—before scaling deployment.