RUSSON, Dylan; AVALOS, Marta; GUERRA-ADAMES, Ariel; GIL-JARDINE, Cedric; LAGARDE, Emmanuel

doi:10.32473/flairs.37

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BPH_Flairs_2024_Russon.pdf (166.0Kb)

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RUSSON, Dylan
Bordeaux population health [BPH]

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

GUERRA-ADAMES, Ariel
Bordeaux population health [BPH]

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Communication dans un congrès

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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

Resumen en inglés

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.< Leer menos

Palabras clave en inglés

Human/AI bias

Systematic discrimination issues via AI

Responsible AI

Data diversity and representation

Literature survey

AI in healthcare