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]
< Reduce
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
Language
EN
Communication dans un congrès
This item was published in
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
English Abstract
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 ...Read more >
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.Read less <
English Keywords
Human/AI bias
Systematic discrimination issues via AI
Responsible AI
Data diversity and representation
Literature survey
AI in healthcare