Development of an AI-Powered Tool to Accelerate and Enhance Systematic Literature Reviews for Evidence-Based Decision-Making in Clinical Research
Accelerating Systematic Literature Reviews with an AI-Powered Screening Tool At ISPOR Europe 2025, Paul Loustalot from Quinten Health presented a new AI-powered tool designed to accelerate and enhance systematic literature reviews (SLRs) by semi-automating title and abstract screening. The tool enhances efficiency while preserving the scientific rigor required in evidence-based clinical research. Paul Loustalot presenting […]
An AI-Powered Tool to Identify and Assess Fit-for-Use Registries for Drug Development and Evaluation
At ISPOR Europe 2025, Quinten Health introduced an AI-powered tool, a deliverable of the More-EUROPA consortium, designed to make real-world data discovery faster, smarter, and more transparent, supporting regulators, HTA bodies, and researchers to identify, assess, and select fit-for-use registries for evidence generation across the drug and product development lifecycles. Leveraging AI to Identify and […]
Development of an AI-Powered Tool to Accelerate and Enhance Systematic Literature Reviews for Evidence-Based Decision Making in Clinical Research
Event: ISPOR Europe 2025, Glasgow, Scotland, UK Authors: Paul Loustalot, Boris Kopin, Sacha Levy, Basile Ferry, Vincent Martenot View Poster OBJECTIVES Systematic literature reviews (SLRs) are foundational for evidence-based decisions-making across the pharmaceutical and medical devices product lifecycle, from early research and development to market launch, and post-market activities. Conventional SLRs are often time- and resource-intensive, […]
Patient-reported outcomes and treatment adherence in type 2 diabetes using natural language processing: Wave 8 of the Observational International Diabetes Management Practices Study
Publisher: Journal of Diabetes Investigation Authors: Juliana CN Chan, Jean Claude Mbanya, Jean-Marc Chantelot, Marina Shestakova, Ambady Ramachandran, Hasan Ilkova, Lucille Deplante, Melissa Rollot, Lydie Melas-Melt, Juan Jose Gagliardino, Pablo Aschner View Publication Aims/Introduction We analyzed patient-reported outcomes of people with type 2 diabetes to better understand perceptions and experiences contributing to treatment adherence. Materials and […]
An AI powered tool to identify and assess fit-for use registries for real-world evidence
Event: EuroDURG 2025, Uppsala, Sweden Authors: Ghinwa Y. Hayek, Pascal Godbillot, Coriande Clemente, Sonia Zebachi, Gaëtan Pinon, Boris Kopin, Elisabeth Bakker, Sieta T. de Vries, Peter G.M. Mol, Billy Amzal View publication Abstract The selection of appropriate real-world data (RWD) sources, particularly registries, is of primary concern for academia, industry, regulators, and health technology assessment […]
The Open-Source Revolution of AliBERT in French Biomedical AI
Developed by Quinten in 2022, AliBERT is a specialized model focusing on the French biomedical language. One version has been released as an open-source tool on the Huggingface platform, marking a significant contribution to the field of natural language processing (NLP) in healthcare.
AliBERT achieves it first successful application in oncology!
A successful proof-of-concept for AliBERT, the first French-language model specialized in the biomedical field. In partnership with a major French Cancer Fighting Institute, Quinten’s datalab team has developed a first concrete use case : the extraction of concepts and structured information from medical reports in oncology. A Natural Language Processing [NLP] task which, until now, has been highly complex, given the technical nature, diversity and specialization of these medical reports.
AliBERT: A pretrained Language Model for French Biomedical Text
Event : BioNLP 2023 Workshop at ACL (Association for Computational Linguistics)Authors: A. Berhe, G. Draznieks, V. Martenot, V. Masdeu, L. Davy, J-D. Zucker View publication Background Over the past few years, domain specific pretrained language models have been investigated and have shown remarkable achievements in different downstream tasks, especially in biomedical domain. These achievements stem on the well known BERT […]
AliBERT : the first pretrained language model for French biomedical text
The paper “AliBERT : A pretrained language model for French biomedical text” was written in collaboration with Aman Berhe, Guillaume Draznieks, Vincent Martenot, Valentin Masdeu, Lucas Davy and Jean-Daniel Zucker. BERT architecture, which allow for context learning on text documents, is mostly trained on common English text resources.Performances in other languages, especially in specific topics which requires deep knowledge and vocabulary, are […]
LiSA : an assisted literature search pipeline for detecting serious adverse drug events with deep learning
Publisher: BMC Medical Informatics and Decision MakingAuthors: V. Martenot, V. Masdeu, J. Cupe, F. Gehin, M. Blanchon, J. Dauriat, A. Horst, M. Renaudin, P. Girard, J-D. Zucker View publication Introduction Detecting safety signals attributed to a drug in scientific literature is a fundamental issue in pharmacovigilance. The constant increase in the volume of publications requires the automation of this tedious task, […]
