Papers by Francois Beaulieu
Empowering Healthcare Practitioners with Language Models: Structuring Speech Transcripts in Two Real-World Clinical Applications (2025.emnlp-industry)
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Jean-Philippe Corbeil, Asma Ben Abacha, George Michalopoulos, Phillip Swazinna, Miguel Del-Agua, Jerome Tremblay, Akila Jeeson Daniel, Cari Bader, Kevin Cho, Pooja Krishnan, Nathan Bodenstab, Thomas Lin, Wenxuan Teng, Francois Beaulieu, Paul Vozila
| Challenge: | Large language models (LLMs) have demonstrated strong performance on clinical natural language processing tasks across multiple medical benchmarks. |
| Approach: | They propose an agentic pipeline for generating realistic, non-sensitive nurse dictations, enabling structured extraction of clinical observations. |
| Outcome: | The proposed pipeline generates realistic, non-sensitive nurse dictations, enabling structured extraction of clinical observations. |
A Modular Approach for Clinical SLMs Driven by Synthetic Data with Pre-Instruction Tuning, Model Merging, and Clinical-Tasks Alignment (2025.acl-long)
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Jean-Philippe Corbeil, Amin Dada, Jean-Michel Attendu, Asma Ben Abacha, Alessandro Sordoni, Lucas Caccia, Francois Beaulieu, Thomas Lin, Jens Kleesiek, Paul Vozila
| Challenge: | Large language models such as GPT-4 have limited their deployment in clinical settings . a novel framework for adapting SLMs into high-performing clinical models is needed . |
| Approach: | They propose a framework for adapting large language models into high-performing clinical models . they pre-instruct experts on relevant medical and clinical corpora and model merging . |
| Outcome: | The proposed framework outperforms the existing model on the CLUE+ benchmark on medical entities and radiology reports. |
MedRiskEval: Medical Risk Evaluation Benchmark of Language Models, On the Importance of User Perspectives in Healthcare Settings (2026.eacl-industry)
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Jean-Philippe Corbeil, Minseon Kim, Maxime Griot, Sheela Agarwal, Alessandro Sordoni, Francois Beaulieu, Paul Vozila
| Challenge: | Existing risk evaluations focused on general safety benchmarks, resulting in role-dependent vulnerabilities in real-world medical and clinical deployments. |
| Approach: | They propose a patient-oriented dataset called PatientSafetyBench that evaluates a variety of open- and closed-source LLMs. |
| Outcome: | The proposed benchmark examines medical risks from 466 open- and closed-source LLMs across 5 risk categories. |