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
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
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
An overview of the landscape of major U.S. neuro-oncology data resources is provided and how these datasets are used in contemporary research is evaluated, including population registries, clinical data networks, federal and consortium research cohorts, institutional datasets, specialized resources, and artificial inte...
Anjali Kapoor, A. Alyakin, J. Markert et al.· Journal of Neuro-Oncology· 0 citations
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