Clinical AI is increasingly integrated into healthcare, yet conventional patient safety frameworks struggle to manage unique risks such as bias, performance drift, and clinician overreliance. The invisibility of AI systems to patients and the inconsistent understanding by healthcare providers further complicate risk detection. The featured white paper advocates for a transformative approach to risk management, shifting from reactive strategies to proactive, lifecycle-based governance. Health systems are encouraged to develop formal AI governance with clear accountability, maintain comprehensive AI tool inventories, and incorporate AI monitoring into existing safety reporting protocols. Recognizing current gaps—such as a lack of standardized safety practices and fragmented regulatory oversight—the paper highlights the need for cross-system collaboration and improved tracking of safety events. Policymakers are urged to define regulatory expectations and promote shared learning to bridge the “AI divide.” Ultimately, ensuring safe AI implementation requires robust governance and accountability frameworks to enhance patient outcomes while prioritizing safety.
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