Medical Language Processing in the era of Large Language Models (MLP-LLM 2025)

Venue : Colocated with CORIA-TALN 2025, Marseille, France

Date : 30th June 2025

[Last Update : 12th March 2025]

[Go to fr version]

Description

The advent of large language models (LLMs) has revolutionized natural language processing across various domains, including healthcare [3,4,7]. However, the complexities of medical language—marked by specialized terminologies, the use of abbreviations and code ontologies such as ICD, UMLS or SNOMED[1], implicit contextual dependencies [8] (based on the context, the medication change information may be different: temporality, action, certainty, etc.) —pose unique challenges and opportunities. Medical NLP is at critical stakes, given the importance of finding the right diagnosis and treatment for each patient [5]. However, the field of health includes not only the human aspect represented by the practitioner-patient relationship, but also the contact with the biological world (animals, plants, viruses, microbes) [10]. This workshop, MLP-LLM, aims to bring together researchers from NLP, medicine, bioNLP and linguistics to explore advancements, limitations, and ethical considerations of using LLMs in medical contexts. Topics of interest include, but are not limited to:

• Fine-tuning and adapting LLMs for medical applications and for different languages.
• Addressing biases in medical language understanding along with LLM hallucinations.
• Proposing evaluation methods to assess the quality of medical NLP tools.
• Ensuring transparency, interpretability, and uncertainty awareness [9] in medical AI systems.
• Developing domain-specific benchmarks for evaluating LLMs in healthcare.
• Developing applications for LLMs in clinical decision support, medical transcription and communication between practitioners and patients. [2,7]

We welcome articles that are:

• new contributions,
• state-of-the-art articles,
• work in progress,
• short/translated version of a paper accepted at a major conference.

Invited Speaker

Natalia Grabar, Université de Lille

Important Dates

• Submission Due: 30 April 2025
• Author Notification: 15 May 2025
• Camera ready: 21 May 2025
• Workshop: 30 June 2025

Contact

mlpllm2025@gmail.com

Programme Committee

• Ioana Buhnila, ATILF, CNRS - Université de Lorraine (co-organizer)
• Aman Sinha, IECL- ATILF - ICANS Strasbourg (co-organizer)
• Hanbyul Song, ATILF, CNRS - Université de Lorraine
• Laura Zanella, POSOS
• Salomé Klein, LiLPa, Université de Strasbourg
• Joé Laroche, LiLPa, Université de Strasbourg
• Delphine Charuau, Trinity College Dublin
• Sam Bigeard, INRIA, Université de Lorraine

Scientific Committee

• Aurélie Névéol, Université Paris-Saclay, CNRS, LISN
• Natalia Grabar, Université de Lille
• Remi Cardon, CENTAL, IL&C, Université Catholique de Louvain
• Cyril Grouin, LISN, CNRS - Université Paris-Saclay
• Amalia Todirascu, LiLPa, Université de Strasbourg
• Lina F. Soualmia, Université Rouen Normandie
• Patrick Watrin, CENTAL, UCLouvain
• Adrien Coulet, Inria Paris - Université de Lorraine
• Benoit Favre, Université d’Aix-Marseille
• Emmanuel Morin, Université de Nantes
• Richard Dufour, Université de Nantes
• Mathieu Constant, Université de Lorraine
• Timothee Mickus, University of Helsinki
• Claire Nedellec, Institut National de Recherche Agronomique (INRA)
• Lisa Raithel, DFKI
• Ioana Buhnila, ATILF, CNRS - Université de Lorraine
• Aman Sinha, IECL- ATILF - ICANS

References

1. Roger A Cote. 1998. Systematized nomenclature of human and veterinary medicine: Snomed international. version 3.5. Northfield, IL: College of American Pathologists.
2. Thomas W LeBlanc, Ashley Hesson, Andrew Williams, Chris Feudtner, Margaret Holmes-Rovner, Lillie D Williamson, and Peter A Ubel. 2014. Patient understanding of medical jargon: a survey study of us medical students. Patient education and counseling, 95(2):238–242.
3. Li, J., Dada, A., Puladi, B., Kleesiek, J., & Egger, J. (2024). ChatGPT in healthcare: a taxonomy and systematic review. Computer Methods and Programs in Biomedicine, 108013.
4. Neveol, A., De Bruijn, B., & Fredouille, C. (2020). TAL et Santé [NLP and Health]. Traitement Automatique des Langues, 61(2), 7-14.
5. Logesh Kumar Umapathi, Ankit Pal, and Malaikannan Sankarasubbu. 2023. Med-halt: Medical domain hallucination test for large language models. arXiv preprint arXiv:2307.15343.
6. Seyedeh Belin Tavakoly Sany, Fatemeh Behzhad, Gordon Ferns, and Nooshin Peyman. 2020. Communication skills training for physicians improves health literacy and medical outcomes among patients with hypertension: a randomized controlled trial. BMC health services research, 20:1–10.
7. Wen, A., Fu, S., Moon, S., El Wazir, M., Rosenbaum, A., Kaggal, V. C., … & Fan, J. (2019). Desiderata for delivering NLP to accelerate healthcare AI advancement and a Mayo Clinic NLP-as-a-service implementation. NPJ digital medicine, 2(1), 130.
8. Mahajan, D., Liang, J. J., & Tsou, C. H. (2022, February). Toward understanding clinical context of medication change events in clinical narratives. In AMIA Annual Symposium Proceedings (Vol. 2021, p. 833).
9. Nazi, Z. A., & Peng, W. (2024, August). Large language models in healthcare and medical domain: A review. In Informatics (Vol. 11, No. 3, p. 57). MDPI
10. Chaix, E., Deléger, L., Bossy, R., & Nédellec, C. (2019). Text mining tools for extracting information about microbial biodiversity in food. Food microbiology, 81, 63-75.