Why Healthcare AI Must Be Engineered to Fail Safely, Not Just Perform Well

Healthcare AI systems operate in unpredictable environments where data can be incomplete, models may encounter unfamiliar cases, and external services can fail, making failure handling a core architectural requirement. A proposed framework layers input validation, model inference, uncertainty assessment, safety boundaries, and human escalation to manage these risks systematically. For agentic AI systems — which access multiple tools and data sources — the failure surface is larger, requiring explicit action boundaries and human approval before any high-stakes decisions are executed. Engineers are urged to test specifically for failure scenarios, such as missing variables, conflicting data sources, or clinician overrides, which standard validation methods often overlook. The overarching goal is not to build perfect systems, but to design ones that degrade gracefully and recover reliably when things go wrong.