arXiv：cs.CL· Krithik Vishwanath, Brandon Ye, Anton Alyakin, John E. Markert, Aaron Hsieh, Micha{\l} Ma\'nkowski, Eric K. Oermann·· 6 小时前AI 评分51
arXiv 论文：无关信息污染病历并干扰大语言模型的临床推理
Incidental information contaminates patient notes and disrupts clinical reasoning in large language models
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arXiv 论文（arXiv:2610.08585）研究无关信息对 LLM 环境记录与临床推理的影响。
正文
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, while mean quality scores changed by at most 0.20 points on five-point scales. In 3.7% of frontier notes, models misattributed the asides or used them clinically. In 57 mock recorded consultations, background speech from a separate patient encounter at -10 dB leaked into 48.2% of transcripts, with contamination detected in 5.3% of downstream notes generated by four open-weight models. We propose a dual encoding hypothesis of clinical reasoning and distraction in LLMs, with preliminary evidence that LLM components associated with disruption by incidental information also support clinical reasoning. These findings support evaluating resistance to incidental information before clinical use, with safeguards that prevent contamination while preserving clinical reasoning.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.08585 [cs.CL] |
| (or arXiv:2610.08585v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08585 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Krithik Vishwanath Mr. [view email]
[v1]
Tue, 6 Oct 2026 15:55:29 UTC (4,129 KB)
来源：arXiv：cs.CL · arxiv.org