11 citations · 32 across the 5 of their papers we have counts for
5 papers
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…
MEDEC: A Benchmark for Medical Error Detection and Correction in Clinical Notes
Asma Ben Abacha, Wen-wai Yim, Yujuan Fu +4
Several studies showed that Large Language Models (LLMs) can answer medical questions correctly, even outperforming the average human score in some medical exams. However, to our k…
ACI-BENCH: a Novel Ambient Clinical Intelligence Dataset for Benchmarking Automatic Visit Note Generation
Wen-wai Yim, Yujuan Fu, Asma Ben Abacha +3
Recent immense breakthroughs in generative models such as in GPT4 have precipitated re-imagined ubiquitous usage of these models in all applications. One area that can benefit by i…
An Investigation of Evaluation Metrics for Automated Medical Note Generation
Asma Ben Abacha, Wen-wai Yim, George Michalopoulos +1
Recent studies on automatic note generation have shown that doctors can save significant amounts of time when using automatic clinical note generation (Knoll et al., 2022). Summari…