Evidence-Bound Reasoning: Neuro-Semantic Verification of Biomedical AI in Glioblastoma Radiogenomics
Background: Biomedical AI can generate plausible explanations without reliably verifying whether each statement is supported by patient-specific evidence.
ProofPaper ↗
Key points
- We developed a neuro-semantic verification framework that converts radiomic measurements into addressable evidence records and machine-checkable claims.
- Methods: UPenn-GBM radiomics were aligned with de novo CaPTk extraction from standardized MRI and expert-validated segmentations in an independent multicenter cohort.
- Reference-defined semantic states were derived from 611 UPenn cases.
- We evaluated cross-cohort transportability, model-linked provenance, deterministic verification, controlled predictive degradation, and an LLM claim-extraction pilot; MGMT prediction served only as a transport stress test.
Sources (1)
- [1]Evidence-Bound Reasoning: Neuro-Semantic Verification of Biomedical AI in Glioblastoma RadiogenomicsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 04:44 PM
Background: Biomedical AI can generate plausible explanations without reliably verifying whether each statement is supported by patient-specific evidence.
We developed a neuro-semantic verification framework that converts radiomic measurements into addressable evidence records and machine-checkable claims.
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