A safe fallback path is a predefined alternative workflow used when an AI-supported process is unavailable, inaccurate, or untrusted. In healthcare, that might mean routing a task to a human reviewer, switching to a manual checklist, or reverting to a simpler validated system. The key is that the backup process is designed in advance, not improvised during an outage.
Fallback paths matter because AI can fail in subtle ways: it may drift, return low-confidence output, be disrupted by an attack, or behave differently after an update. A good fallback preserves patient safety and operational continuity by defining triggers, responsibilities, and limits for when automation must stop. In security planning, this reduces the chance that a compromised or unreliable model quietly affects care. In practice, teams test these paths through drills, monitoring rules, and change management so staff know exactly how to continue safely when AI cannot be trusted.