arXiv：cs.CL· Mariya Miteva, Maria Nisheva-Pavlova·· 6 小时前AI 评分37
面向胶质母细胞瘤影像基因组学的生物医学 AI 神经语义验证框架
Evidence-Bound Reasoning: Neuro-Semantic Verification of Biomedical AI in Glioblastoma Radiogenomics
AI 导读
研究团队提出神经语义验证框架，将影像组学测量转为可寻址证据记录与机器可校验声明，并在 611 例 UPenn 病例上定义语义状态，语义状态一致率中位数为 0.786（加权 kappa 0.709）。
正文
Abstract: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. Methods: UPenn-GBM radiomics were aligned with de novo CaPTk extraction from standardized MRI and expert-validated segmentations in an independent multicenter cohort. The shared space comprised 1,728 features from T1, T1GD, T2, and FLAIR MRI across three tumor regions. 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. Results: Median semantic-state agreement was 0.786 (weighted kappa 0.709), ranging from 0.918 for morphologic to 0.252 for intensity features. The external evidence ledger contained 1,655 model-linked records for 331 patients. The verifier achieved 100% exact-set accuracy in a 6,620-claim corruption benchmark. In a 24-case pilot, GPT-5.6 Sol reproduced 72/72 prespecified atomic claims, and the frozen verifier recovered 24/24 expected conditions. During controlled degradation, ROC AUC declined from 0.899 to 0.500 while verification accuracy remained 1.000. External MGMT discrimination was weak (ROC AUC 0.543). Conclusions: Verifiability can be engineered and evaluated independently of predictive performance. LLMs may structure explanations, while final evidence-consistency checking remains deterministic.
| Comments: | 15 pages, 4 figures, 4 tables. Preprint |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.08660 [cs.CL] |
| (or arXiv:2610.08660v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08660 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Mariya Miteva Dr. [view email]
[v1]
Tue, 6 Oct 2026 16:44:02 UTC (776 KB)
来源：arXiv：cs.CL · arxiv.org