AI-Driven Emergency Patient Flow Optimization is Both an Unmissable Opportunity and a Risk of Systematizing Health Disparities
AVALOS, Marta
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
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Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
AVALOS, Marta
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
< Réduire
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
Langue
EN
Communication dans un congrès
Ce document a été publié dans
Proceedings of FLAIRS-37, May 19-21, Sandestin Beach, FL, FLAIRS-37 2024 - 37th International FLAIRS Conference, 2024-05-19, Miramar Beach. 2024-05-01, vol. 37, p. 4
LibraryPress@UF
Résumé en anglais
There is a burgeoning interest in harnessing artificial intelligence (AI) to enhance patient flow within emergency departments (EDs). However, this advancement is accompanied by a significant risk: by relying on historical ...Lire la suite >
There is a burgeoning interest in harnessing artificial intelligence (AI) to enhance patient flow within emergency departments (EDs). However, this advancement is accompanied by a significant risk: by relying on historical healthcare data, these AI tools may perpetuate existing systemic biases associated with gender, age, ethnicity, and socioeconomic status. This paper surveys studies identifying biases in ED data, offering context for concern about these biases. These insights are valuable for researchers developing AI to optimize ED workflows while accounting for ethical considerations.< Réduire
Mots clés en anglais
Human/AI bias
Systematic discrimination issues via AI
Responsible AI
Data diversity and representation
Literature survey
AI in healthcare
Unités de recherche