Here’s a scenario that should make any quality leader in pharma or medical devices uncomfortable: A software team building a diagnostic support tool uses an AI coding assistant to generate a data-processing module. The module looks correct, passes validation testing, and ships. Eighteen months later, under a specific combination of patient data inputs that never appeared in the test set, the module misclassifies results. Nobody on the team can fully explain why because nobody on the team fully wrote it.

This isn’t science fiction. It’s the logical endpoint of a trend already well underway. AI coding assistants now generate an estimated 40% or more of new code in enterprise development environments. In most industries, that raises productivity questions. In regulated industries such as pharma, medical devices, and clinical software, it raises something more serious: a fundamental challenge to the traceability, validation, and accountability requirements that underpin product safety.

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