LLM Judges Verify Presence, Not Absence: Omission Blindness in AI Clinical Notes
A new research paper explores 'omission blindness' in Large Language Models (LLMs) when analyzing clinical notes, noting that these models are better at verifying the presence of information than its absence. The study highlights critical limitations in AI reliability for medical documentation.
Why it matters
Understanding AI limitations in clinical settings is essential for the safe and effective integration of automated tools in healthcare.
Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Sebastian Fox [ view email ] [v1] Mon, 31 Aug 2026 15:59:54 UTC (337 KB) Full-text links: Access Paper: View a PDF of the paper titled LLM Judges Verify Presence, Not Absence: Omission Blindness in AI Clinical Notes and What Recovers It, by Sebastian Fox and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2026-08 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps?
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