13 citations · 26 across the 15 of their papers we have counts for
10 papers · 1 filter
Generalist Large Language Models Outperform Clinical Tools on Medical Benchmarks
Krithik Vishwanath, Mrigayu Ghosh, Anton Alyakin +3
Specialized clinical AI assistants are rapidly entering medical practice, often framed as safer or more reliable than general-purpose large language models (LLMs). Yet, unlike fron…
Generalist Foundation Models Are Not Clinical Enough for Hospital Operations
Lavender Y. Jiang, Angelica Chen, Xu Han +16
Hospitals and healthcare systems rely on operational decisions that determine patient flow, cost, and quality of care. Despite strong performance on medical knowledge and conversat…
On the Relationship Between the Choice of Representation and In-Context Learning
Ioana Marinescu, Kyunghyun Cho, Eric Karl Oermann
In-context learning (ICL) is the ability of a large language model (LLM) to learn a new task from a few demonstrations presented as part of the context. Past studies have attribute…
Clinically Grounded Agent-based Report Evaluation: An Interpretable Metric for Radiology Report Generation
Radhika Dua, Young Joon, Kwon +9
Radiological imaging is central to diagnosis, treatment planning, and clinical decision-making. Vision-language foundation models have spurred interest in automated radiology repor…
Evaluating the performance and fragility of large language models on the self-assessment for neurological surgeons
Krithik Vishwanath, Anton Alyakin, Mrigayu Ghosh +5
The Congress of Neurological Surgeons Self-Assessment for Neurological Surgeons (CNS-SANS) questions are widely used by neurosurgical residents to prepare for written board examina…
Gateformer: Advancing Multivariate Time Series Forecasting through Temporal and Variate-Wise Attention with Gated Representations
Yu-Hsiang Lan, Eric K. Oermann
There has been a recent surge of interest in time series modeling using the Transformer architecture. However, forecasting multivariate time series with Transformer presents a uniq…