activity
20192026
most citedRetrieval-Augmented Transformer-XL for Close-Domain Dialog Generation

10 citations · 35 across the 10 of their papers we have counts for

collaborators
Showing 2025Show all

6 papers · 1 filter

eess.IV2025

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…

cs.AI2025★ 1 cited

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…

cs.CL2025★ 1 cited

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…

cs.CL2025

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…

cs.CL2025★ 9 cited

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…

cs.AI2025

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…