12 papers
ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?
Canyu Chen, Jian Yu, Shan Chen +8
Large Language Models (LLMs) hold great promise to revolutionize current clinical systems for their superior capacities on medical text processing tasks and medical licensing exams…
Can Language Models Identify Side Effects of Breast Cancer Radiation Treatments?
Natalie Seah, Danielle S. Bitterman, Daphna Spiegel +1
Accurately communicating the side effects of cancer treatments to cancer survivors is critical, particularly in settings such as informed consent, where clinicians must clearly and…
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…
Proof of Time: A Benchmark for Evaluating Scientific Idea Judgments
Bingyang Ye, Shan Chen, Jingxuan Tu +4
Large language models are increasingly being used to assess and forecast research ideas, yet we lack scalable ways to evaluate the quality of models' judgments about these scientif…
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…
Simulated patient systems powered by large language model-based AI agents offer potential for transforming medical education
Huizi Yu, Jiayan Zhou, Lingyao Li +22
Background: Simulated patient systems are important in medical education and research, providing safe, integrative training environments and supporting clinical decision making. Ad…