Engineering Healthcare AI for Production, Not Just Accuracy

Healthcare AI can perform well in testing and still fail in production when regulation, interoperability, clinical workflows, and lifecycle controls are treated as later-stage concerns. The article argues that validation, data governance, EHR integration, explainability, monitoring, cybersecurity, and regulatory evidence must be designed into delivery from the first sprint. It highlights FHIR integration, model drift, human oversight, and post-market surveillance as recurring engineering requirements. Technology leaders should evaluate healthcare AI as a regulated production system, not primarily as a model-development exercise.