Detecting Human Bias in Emergency Triage Using LLMs
The surge in AI-based research for emergency healthcare presents 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 a chance for a paradigm shift, act...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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LibraryPress@UF
2024-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Subjects: | |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/135586 |
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| _version_ | 1849762766318993408 |
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| author | Marta Avalos Dalia Cohen Dylan Russon Melissa Davids Oceane Doremus Gabrielle Chenais Eric Tellier Cédric Gil-Jardiné Emmanuel Lagarde |
| author_facet | Marta Avalos Dalia Cohen Dylan Russon Melissa Davids Oceane Doremus Gabrielle Chenais Eric Tellier Cédric Gil-Jardiné Emmanuel Lagarde |
| author_sort | Marta Avalos |
| collection | DOAJ |
| description | The surge in AI-based research for emergency healthcare presents 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 a chance for a paradigm shift, acting as a tool to counteract human biases.
Our study focuses on emergency triage, swiftly 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. Additionally, we analyze triage data descriptively and pilot AI-driven triage using a Large Language Model model with data from University Hospital of Bordeaux. Finally, assembling these pieces, we outline an experimental plan to assess AI effectiveness in detecting biases in triage data. |
| format | Article |
| id | doaj-art-40a03d7badbb4e578ce8dacd7c9c3e4f |
| institution | DOAJ |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-40a03d7badbb4e578ce8dacd7c9c3e4f2025-08-20T03:05:39ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622024-05-013710.32473/flairs.37.1.13558671965Detecting Human Bias in Emergency Triage Using LLMsMarta Avalos0https://orcid.org/0000-0002-5471-2615Dalia CohenDylan RussonMelissa DavidsOceane DoremusGabrielle ChenaisEric TellierCédric Gil-JardinéEmmanuel LagardeUniversity of BordeauxThe surge in AI-based research for emergency healthcare presents 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 a chance for a paradigm shift, acting as a tool to counteract human biases. Our study focuses on emergency triage, swiftly 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. Additionally, we analyze triage data descriptively and pilot AI-driven triage using a Large Language Model model with data from University Hospital of Bordeaux. Finally, assembling these pieces, we outline an experimental plan to assess AI effectiveness in detecting biases in triage data.https://journals.flvc.org/FLAIRS/article/view/135586human biaslarge language modelsnatural language processingemergency departmenttriage |
| spellingShingle | Marta Avalos Dalia Cohen Dylan Russon Melissa Davids Oceane Doremus Gabrielle Chenais Eric Tellier Cédric Gil-Jardiné Emmanuel Lagarde Detecting Human Bias in Emergency Triage Using LLMs Proceedings of the International Florida Artificial Intelligence Research Society Conference human bias large language models natural language processing emergency department triage |
| title | Detecting Human Bias in Emergency Triage Using LLMs |
| title_full | Detecting Human Bias in Emergency Triage Using LLMs |
| title_fullStr | Detecting Human Bias in Emergency Triage Using LLMs |
| title_full_unstemmed | Detecting Human Bias in Emergency Triage Using LLMs |
| title_short | Detecting Human Bias in Emergency Triage Using LLMs |
| title_sort | detecting human bias in emergency triage using llms |
| topic | human bias large language models natural language processing emergency department triage |
| url | https://journals.flvc.org/FLAIRS/article/view/135586 |
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