10 citations · 35 across the 10 of their papers we have counts for
10 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…
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…
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…
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…
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…
A Modular Approach for Clinical SLMs Driven by Synthetic Data with Pre-Instruction Tuning, Model Merging, and Clinical-Tasks Alignment
Jean-Philippe Corbeil, Amin Dada, Jean-Michel Attendu +7
High computation costs and latency of large language models such as GPT-4 have limited their deployment in clinical settings. Small language models (SLMs) offer a cost-effective al…
X-Reasoner: Towards Generalizable Reasoning Across Modalities and Domains
Qianchu Liu, Sheng Zhang, Guanghui Qin +9
Recent proprietary models (e.g., o3) have begun to demonstrate strong multimodal reasoning capabilities. Yet, most existing open-source research concentrates on training text-only…
Contextual Density Ratio for Language Model Biasing of Sequence to Sequence ASR Systems
Jesús Andrés-Ferrer, Dario Albesano, Puming Zhan +1
End-2-end (E2E) models have become increasingly popular in some ASR tasks because of their performance and advantages. These E2E models directly approximate the posterior distribut…
Retrieval-Augmented Transformer-XL for Close-Domain Dialog Generation
Giovanni Bonetta, Rossella Cancelliere, Ding Liu +1
Transformer-based models have demonstrated excellent capabilities of capturing patterns and structures in natural language generation and achieved state-of-the-art results in many…
DeepAAA: clinically applicable and generalizable detection of abdominal aortic aneurysm using deep learning
Jen-Tang Lu, Rupert Brooks, Stefan Hahn +9
We propose a deep learning-based technique for detection and quantification of abdominal aortic aneurysms (AAAs). The condition, which leads to more than 10,000 deaths per year in…