VVUQ Physical AI Oncology Trial Bill

The VVUQ Physical AI Oncology Trial Bill (H.R. 9510) is a proposed United States law requiring an automated verification, validation, and uncertainty quantification (VVUQ) process to clear robot-patient interaction code before that code is generated or executed in a Physical AI oncology clinical trial. Its thesis is rigor over the code itself: verification ahead of generation is the patient-safety and efficacy control, and repository-based AI models synthesise VVUQ code generation and execution into public, reproducible documents that accelerate legislation. Two author studies show large language models are appropriate for code verification: with the verification fraction held at exactly 1.0 across 51 of 51 tests; the other proves practical VVUQ generation and execution against fourteen external standards and two clinical baselines, with 172 of 172 tests passing. Second, a national platform supplies the Unification and Physical AI Standard Levels and five trial, sponsor, and mobile simulations that make the robot-patient case concrete. Third, the bill binds these to current federal and state law. Codifying verification before generation ensures the United States is first in oncology clinical trial safety.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-30
DOI
https://doi.org/10.5281/zenodo.20454869
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
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article

VVUQ Physical AI Oncology Trial Bill

Kevin Kawchak
Zenodo (CERN European Organization for Nuclear Research)
Artificial Intelligence in Healthcare and Education
article

VVUQ Physical AI Oncology Trial Bill

Kevin Kawchak
article en

Abstract

The VVUQ Physical AI Oncology Trial Bill (H.R. 9510) is a proposed United States law requiring an automated verification, validation, and uncertainty quantification (VVUQ) process to clear robot-patient interaction code before that code is generated or executed in a Physical AI oncology clinical trial. Its thesis is rigor over the code itself: verification ahead of generation is the patient-safety and efficacy control, and repository-based AI models synthesise VVUQ code generation and execution into public, reproducible documents that accelerate legislation. Two author studies show large language models are appropriate for code verification: with the verification fraction held at exactly 1.0 across 51 of 51 tests; the other proves practical VVUQ generation and execution against fourteen external standards and two clinical baselines, with 172 of 172 tests passing. Second, a national platform supplies the Unification and Physical AI Standard Levels and five trial, sponsor, and mobile simulations that make the robot-patient case concrete. Third, the bill binds these to current federal and state law. Codifying verification before generation ensures the United States is first in oncology clinical trial safety.

Zenodo (CERN European Organization for Nuclear Research)
Peace, Justice and strong institutions
Openalex Percentile: Top 8%
Artificial Intelligence in Healthcare and Education
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VVUQ Physical AI Oncology Trial Bill — Kevin Kawchak · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS