Innovative research group dedicated to improving emergency triage and patient care through Artificial Intelligence. Led by Dr Edouard Lansiaux, we develop solutions that save lives.
Discover our projectsResearch projects
Patients enrolled
AI concordance
Partner centers
A progressive and complementary approach to revolutionize emergency triage
Single-center retrospective study (n=657, Lille University Hospital) demonstrating superiority of 3 AI models (NLP, LLM, JEPA) over standard nursing triage. Concordance of 90% vs 30% (p<0.001).
Publication: IEEE BDCAT 2025, JMIR Medical Informatics
OngoingExternal validation across 5 centers (CHU Lille, CH Douai, Denain, Maubeuge, Tenon AP-HP) with several thousand patients. Period: January-February 2026.
Type: Prospective observational study
Funding pendingProspective cluster-randomized bicentric trial (CHU Lille, CH Maubeuge) evaluating AI assistance under real-world conditions. Objective: concordance increase from 68% to 76%.
Funding requested: PHRC-I & doctoral grant
OngoingMultidimensional comparative evaluation of three AI architectures (NLP, LLM, JEPA) for emergency triage, with hybrid Process Mining simulation and digital twins.
Approach: DES Simulation + Multi-Agent Systems
International collaborationRedirection application integrating AI to identify cases suitable for primary care. Estimated 15-20% reduction in emergency department overcrowding.
Partners: CHU Quebec, Mila, IVADO
OngoingProspective study comparing a GPT model against call dispatchers and dispatch physicians at the SAS-Centre 15 on 60 simulated scenarios. Double-blind design with complete crossover evaluation.
Center: SAS-Centre 15, Lille University Hospital
International collaborationInternational multicenter federated study evaluating an innovative triage effectiveness metric in emergency departments. Federated analysis with row-level data processed locally.
Partners: Lund Univ. 🇸🇪, Yale 🇺🇸, UVA 🇺🇸
Lansiaux E, Leman M.
Lansiaux E, Jairi I, Zgaya-Biau H.
Lansiaux E.
Lansiaux E, Leman M, Ammi M.
Lansiaux E, Marx J, Tataru G.
Lansiaux E, Guerif Dubreucq E, Chan T.
Lansiaux E, Simonet A, Wiel E.
Lansiaux E, Azzouz R, Chazard E, Vromant A, Wiel E.
Lansiaux E, Azzouz R, Chazard E, Vromant A, Wiel E.
Lansiaux E, Arnaud E, Arrouy L, Auboiroux PH, Balaz PA, Baron MA, Depil-Duval A, Dubreucq-Guérif E, Dumontier T, Ellouze S, Gil-Jardine C, Gilbert A, Heidet M, Mpela AG, Lemaitre EL, Vromant A, Violeau M.
Lansiaux E.
Médecine de Catastrophe - Urgences Collectives, 9(2), 96-100
Lansiaux E, Baron MA, Vromant A.
At EUSEM 2025 in Vienna, this session explores how AI is transforming patient reception and triage in emergency departments.
Faced with increased ED activity, can AI one day supplement nursing expertise at the emergency department entrance?
Between technological innovation, operational pressure, and ethical challenges — the growing role of AI in emergency services in 2025.
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Triage accuracy improvement from 30% to 90%
Reduction of dangerous under-triage for patients
Optimization of hospital resources
International collaborations France-Canada-Sweden-USA