Incidental information contaminates patient notes and disrupts clinical reasoning in large language models

Paper recorded by Signals 4 on 2026-10-06 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-06 on arXiv · recorded by Signals 4 on 2026-10-07

Category: cs.CL · 自然语言处理 · first seen 2026-10-07

Abstract

Large language models (LLMs) are increasingly relied upon to support ambient documentation and clinical reasoning. Here we examine the impact of a failure mode shared between these two applications by assessing their sensitivity to information incidental to the patient encounter. In 576 patient-clinician dialogues, we found that frontier models inserted small-talk exchanges into 35% of notes, whil

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