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The Engineering Debt Behind AI-Built Healthcare MVPs
Five evidence gates can help leaders decide what to retain, what to redesign and what must wait before real patient information enters a product. By Silvio Sampaio A healthcare prototype can demonstrate a compelling workflow without demonstrating that the organization can operate it responsibly. Before release, leaders need answers to less visible questions: who can retrieve a record, what happens when an integration fails, and can the team recover after an outage? The proposed assessment uses five gates: data boundaries, authorization, clinical integration, recovery and runtime AI. It is a release framework, not a compliance certification or a claim that AI-generated software is inherently unsafe. Separate two AI questions AI-assisted development and runtime AI create different review scopes. For generated application code, assess architecture, dependencies, tests and maintainability. For an application that also calls a model in production, assess the information it receives and the actions its output can influence. Do not allow a successful demonstration in one scope to stand in for evidence in the other. Assign owners and acceptance criteria separately. Gate one: account...
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