Brandon Ye

Preprint Oct 2026

Large language models are vulnerable to incidental information in clinical documentation and reasoning

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...

K. Vishwanath, Brandon Ye, A. Alyakin et al. · 0 citations

#natural language process... Preprint Oct 2026

Clinician use of language models diverges from how the models are evaluated

Large language model (LLM) assistants are being deployed to clinicians across health systems, and judgments about their readiness rest largely on benchmark scores, most of them derived from examination questions or curated cases. A benchmark predicts performance in deployment only to the extent that its items resemble...

K. Vishwanath, Hai-Tong Lin, A. Alyakin et al. · 0 citations

#natural language process... Preprint Oct 2026

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

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...

K. Vishwanath, Brandon Ye, A. Alyakin et al. · 0 citations