6 papers
A safety-oriented hypothetico-deductive framework for AI-assisted differential diagnosis
Fan Ma, Mauro Giuffrè, Donald Wright +12
Diagnostic error is a major threat to patient safety, yet current large language model (LLM) systems often treat diagnosis as a one-shot prediction task, lacking safeguards against…
Foundation Models to Unlock Real-World Evidence from Nationwide Medical Claims
Fan Ma, Yuntian Liu, Xiang Lan +22
Evidence derived from large-scale real-world data (RWD) is increasingly informing regulatory evaluation and healthcare decision-making. Administrative claims provide population-sca…
LAMP: Extracting Local Decision Surfaces From Large Language Models
Ryan Chen, Youngmin Ko, Zeyu Zhang +5
We introduce LAMP (Local Attribution Mapping Probe), a method that shines light onto a black-box language model's decision surface and studies how reliably a model maps its stated…
ctELM: Decoding and Manipulating Embeddings of Clinical Trials with Embedding Language Models
Brian Ondov, Chia-Hsuan Chang, Yujia Zhou +2
Text embeddings have become an essential part of a variety of language applications. However, methods for interpreting, exploring and reversing embedding spaces are limited, reduci…
EHRNavigator: A Multi-Agent System for Patient-Level Clinical Question Answering over Heterogeneous Electronic Health Records
Lingfei Qian, Mauro Giuffre, Yan Wang +11
Clinical decision-making increasingly relies on timely and context-aware access to patient information within Electronic Health Records (EHRs), yet most existing natural language q…
From Compound Figures to Composite Understanding: Developing a Multi-Modal LLM from Biomedical Literature with Medical Multiple-Image Benchmarking and Validation
Zhen Chen, Yihang Fu, Gabriel Madera +5
Multi-modal large language models (MLLMs) have shown promise in advancing healthcare. However, most existing models remain confined to single-image understanding, which greatly lim…