AI-Driven Emergency Patient Flow Optimization is Both an Unmissable Opportunity and a Risk of Systematizing Health Disparities
Human Bias in ED: Implications for AI-Driven Patient Flow Optimization
DOI:
https://doi.org/10.32473/flairs.37.1.135570Palabras clave:
Human/AI bias, Systematic discrimination issues via AI, Responsible AI, Data diversity and representation, Literature survey, AI in healthcareResumen
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.
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Derechos de autor 2024 Dylan Russon, Marta Avalos, Ariel Guerra-Adames, Cédric Gil-Jardiné, Emmanuel Lagarde
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.