8 papers
SchemaRAG: Dynamic Large Schema Reduction for LLM-driven Structured Information Extraction
Sin Yu Bonnie Ho, Arlie Coles, Erik Larsson +3
Extracting structured data from unstructured text using large language models (LLMs) becomes challenging when target schemas are large and complex. In such cases, including the ful…
Less Finetuning, Better Retrieval: Rethinking LLM Adaptation for Biomedical Retrievers via Synthetic Data and Model Merging
Sameh Khattab, Jean-Philippe Corbeil, Osman Alperen KoraÅ +5
Retrieval-augmented generation (RAG) has become the backbone of grounding Large Language Models (LLMs), improving knowledge updates and reducing hallucinations. Recently, LLM-based…
MedRiskEval: Medical Risk Evaluation Benchmark of Language Models, On the Importance of User Perspectives in Healthcare Settings
Jean-Philippe Corbeil, Minseon Kim, Maxime Griot +4
As the performance of large language models (LLMs) continues to advance, their adoption in the medical domain is increasing. However, most existing risk evaluations largely focused…
The Illusion of Readiness in Health AI
Yu Gu, Jingjing Fu, Xiaodong Liu +29
Large language models have demonstrated remarkable performance in a wide range of medical benchmarks. Yet underneath the seemingly promising results lie salient growth areas, espec…
Empowering Healthcare Practitioners with Language Models: Structuring Speech Transcripts in Two Real-World Clinical Applications
Jean-Philippe Corbeil, Asma Ben Abacha, George Michalopoulos +12
Large language models (LLMs) such as GPT-4o and o1 have demonstrated strong performance on clinical natural language processing (NLP) tasks across multiple medical benchmarks. None…
AURAD: Anatomy-Pathology Unified Radiology Synthesis with Progressive Representations
Shuhan Ding, Jingjing Fu, Yu Gu +6
Medical image synthesis has become an essential strategy for augmenting datasets and improving model generalization in data-scarce clinical settings. However, fine-grained and cont…