{"slug": "eviground-ct-sentence-to-volume-grounding-for-verifying-llm-generated-chest-ct", "title": "EviGround-CT: Sentence-to-Volume Grounding for Verifying LLM-Generated Chest CT Reports", "summary": "A new method called EviGround-CT raises evidence-supported precision for LLM-generated chest CT reports from 0.263 to 0.365 across 137 extracted in-scope positive findings, according to an arXiv paper (2610.33038v1). The approach uses a deterministic extractor to identify positive finding sentences and a 3D grounding model to predict an evidence mask for each sentence, dropping the unsupported positive finding rate from 0.737 to 0.635 on a held-out 100-case ReXGroundingCT subset. The paired case-level bootstrap ESP difference was +0.102 (95% confidence interval: [0.028, 0.181]), with predicted masks linking individual report statements to spatial evidence inspectable alongside the CT scan.", "body_md": "arXiv:2610.33038v1 Announce Type: new \nAbstract: Chest computed tomography (CT) reports generated by large language models (LLMs) can describe positive findings that lack localized evidence in the scan. We propose EviGround-CT to verify these findings after report generation. A deterministic extractor identifies positive finding sentences, and a 3D grounding model predicts an evidence mask for each sentence. Using the full sentence retains anatomical location, laterality, and descriptive modifiers in the grounding query. We evaluate report generation on CT-RATE, and grounding and verification on a held-out 100-case ReXGroundingCT subset. Using the available annotations, sentence-grounded verification raises evidence-supported precision (ESP) from 0.263 to 0.365 across 137 extracted in-scope positives, compared with category conditioning. The corresponding unsupported positive finding rate falls from 0.737 to 0.635. The paired case-level bootstrap ESP difference is +0.102 (95% confidence interval: [0.028, 0.181]). The predicted masks associate individual report statements with spatial evidence that can be inspected alongside the CT.", "url": "https://wpnews.pro/news/eviground-ct-sentence-to-volume-grounding-for-verifying-llm-generated-chest-ct", "canonical_source": "https://arxiv.org/abs/2610.33038", "published_at": "2026-10-27 04:00:00+00:00", "updated_at": "2026-10-27 04:12:25.878843+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-research", "computer-vision", "ai-safety"], "entities": ["EviGround-CT", "CT-RATE", "ReXGroundingCT", "arXiv"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/eviground-ct-sentence-to-volume-grounding-for-verifying-llm-generated-chest-ct", "markdown": "https://wpnews.pro/news/eviground-ct-sentence-to-volume-grounding-for-verifying-llm-generated-chest-ct.md", "text": "https://wpnews.pro/news/eviground-ct-sentence-to-volume-grounding-for-verifying-llm-generated-chest-ct.txt", "jsonld": "https://wpnews.pro/news/eviground-ct-sentence-to-volume-grounding-for-verifying-llm-generated-chest-ct.jsonld"}}