AVALOS, Marta; COHEN, Dalia; RUSSON, Dylan; DAVIDS, Melissa; DORÉMUS, Océane; CHENAIS, Gabrielle; TELLIER, Eric; GIL-JARDINE, Cedric; LAGARDE, Emmanuel

Voir/Ouvrir

FLAIRS_37_158.pdf (281.3Ko)

Métadonnées

Afficher la notice complète

Licence d’utilisation du document

AVALOS, Marta
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]

COHEN, Dalia
Bordeaux population health [BPH]

RUSSON, Dylan
Bordeaux population health [BPH]

Langue

Communication dans un congrès

Ce document a été publié dans

Proceedings of FLAIRS-37, FLAIRS 2024 - 37th International Florida Artificial Intelligence Research Society Conference, 2024-05-19, Sandestin Beach. 2024-05-01, vol. 37, p. 6

LibraryPress@UF

Résumé en anglais

The surge in AI-based research for emergency healthcare poses challenges such as data protection compliance and the risk of exacerbating health inequalities. Human biases in demographic data used to train AI systems may indeed be replicated. Yet, AI also offers achance for a paradigm shift, acting as a tool to counteract human biases. Our study focuses on emergency triage, rapidly categorizing patients by severity upon arrival. Objectives include conducting a literature review to identify potential human biases in triage and presenting a preliminary study. This involves a qualitative survey to complement the review on factors influencing triage scores. Moreover, we analyze triage data descriptively and pilot AI-driven triage using an LLM with data from the local hospital. Finally, assembling these pieces, we outline an experimental plan to assess AI’s effectiveness in detecting human biases in triage data.< Réduire

Mots clés en anglais

Emergency Department

Triage

Human Bias

Large Language Models

Natural Language Processing

URI

https://oskar-bordeaux.fr/handle/20.500.12278/201800

Unités de recherche

Bordeaux Population Health Research Center (BPH) - UMR 1219