77 citations · 97 across the 23 of their papers we have counts for
6 papers · 1 filter
Large language models require a new form of oversight: capability-based monitoring
Katherine C. Kellogg, Bingyang Ye, Yifan Hu +3
The rapid adoption of large language models (LLMs) in healthcare has been accompanied by scrutiny of their oversight. Existing monitoring approaches, inherited from traditional mac…
A Field Guide to Deploying AI Agents in Clinical Practice
Jack Gallifant, Katherine C. Kellogg, Matt Butler +19
Large language models (LLMs) integrated into agent-driven workflows hold immense promise for healthcare, yet a significant gap exists between their potential and practical implemen…
MedBrowseComp: Benchmarking Medical Deep Research and Computer Use
Shan Chen, Pedro Moreira, Yuxin Xiao +6
Large language models (LLMs) are increasingly envisioned as decision-support tools in clinical practice, yet safe clinical reasoning demands integrating heterogeneous knowledge bas…
When Models Reason in Your Language: Controlling Thinking Language Comes at the Cost of Accuracy
Jirui Qi, Shan Chen, Zidi Xiong +3
Recent Large Reasoning Models (LRMs) with thinking traces have shown strong performance on English reasoning tasks. However, their ability to think in other languages is less studi…
TheBlueScrubs-v1, a comprehensive curated medical dataset derived from the internet
Luis Felipe, Carlos Garcia, Issam El Naqa +6
The need for robust and diverse data sets to train clinical large language models (cLLMs) is critical given that currently available public repositories often prove too limited in…
Sparse Autoencoder Features for Classifications and Transferability
Jack Gallifant, Shan Chen, Kuleen Sasse +3
Sparse Autoencoders (SAEs) provide potentials for uncovering structured, human-interpretable representations in Large Language Models (LLMs), making them a crucial tool for transpa…