AI clinical resilience is the ability of AI-supported healthcare workflows to stay safe, usable, and recoverable when a model, system, or integration fails or behaves in an unexpected way. In practice, that means care teams can still make sound decisions if an AI tool is unavailable, produces low-confidence output, or is fed bad data.
This matters because clinical AI can influence diagnosis, monitoring, documentation, scheduling, and care coordination. If an attacker tampers with inputs, changes model behavior, or disrupts service, the result may be more than an IT outage: it can become a patient safety issue. Defenses include strong change control, monitoring for drift and abnormal outputs, access controls, backup or manual workflows, and clear escalation paths. Good AI clinical resilience treats cybersecurity, clinical safety, and operational continuity as one problem, not separate ones.