Unification Standard Level for Physical AI Oncology Trials

As physical AI systems advance toward clinical deployment in oncology, no standardized framework exists to evaluate how ready a robotic platform is for unified, multi-site clinical trials. Current technology readiness assessments (e.g., NASA TRL, MLTRL) do not capture the unique demands of cross-platform simulation switching, AI integration, inter-organizational robot progress sharing, and federated regulatory compliance required for multi-site oncology trials. This paper introduces the Unification Standard Level (USL), a 1.0–10.0 scoring framework that evaluates physical AI robots across four equally weighted dimensions: (A) Simulation Framework Switching, (B) Generative/Agentic AI Integration, (C) Cross-Robot Progress Sharing, and (D) Multi-Site Clinical Trial Collaboration. We apply USL to nine robots across three categories—collaborative robots (cobots), surgical robots, and humanoid robots—finding per-dimension scores ranging from 1.5 to 8.5 and final composite scores from 3.4 to 7.4. The Franka Emika Panda (USL 7.4) and da Vinci dVRK (USL 7.1) lead their respective categories, both driven by large open-source ecosystems. Clinical trial readiness (Dimension D) remains the weakest dimension for seven of nine robots evaluated, revealing a field-wide gap between research maturity and clinical deployment infrastructure. All scoring code, robot evaluation modules, and documentation are open-source at https://github.com/kevinkawchak/physical-ai-oncology-trials under MIT license.