VVUQ Oncology Clinical Trial LLM Verification Automation Priority over Existing Generated Code

Physical AI is entering oncology trials through in silico simulation, autonomous code generation, and eventually robotic procedures - settings in which an error that ships costs far more than one caught prior to production. This work reports an end to end pipeline in which Claude Code performs five operations in order. Specifically, it generates code from instructions, executes that code, generates image instructions, generates figures, then assembles a draft, and finally a full paper. The central argument is that none of these generation steps decides whether a deliverable is trustworthy; the verification, validation, and uncertainty quantification (VVUQ) process does. These precautions can also make autonomous physical AI oncology trials faster, less expensive, and more rigorous than conventional verifications. Code execution supports the claim directly in the cancer-automated repository, passing 51 of 51 automated tests across 8 modules and accelerated a representative schedule by a factor of 2.5, from 30 to 12 days. Its VVUQ gate, centered on generated script vvuq/vvuq_gate.pyexercised across 6 cases, accepted 1 of 5 - escalating the blocked cases to a human, and requiring a verification fraction of exactly 1.0.