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- W4387569602 abstract "Artificial intelligence (AI) and machine learning (ML) have an immense potential to transform healthcare as already demonstrated in various medical specialties. This scoping review focuses on the factors that influence health data poverty, by conducting a literature review, analysis, and appraisal of results. Health data poverty is often an unseen factor which leads to perpetuating or exacerbating health disparities. Improvements or failures in addressing health data poverty will directly impact the effectiveness of AI/ML systems. The potential causes are complex and may enter anywhere along the development process. The initial results highlighted studies with common themes of health disparities (72%), AL/ML bias (28%) and biases in input data (18%). To properly evaluate disparities that exist we recommend a strengthened effort to generate unbiased equitable data, improved understanding of the limitations of AI/ML tools, and rigorous regulation with continuous monitoring of the clinical outcomes of deployed tools." @default.
- W4387569602 created "2023-10-13" @default.
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- W4387569602 date "2023-10-12" @default.
- W4387569602 modified "2023-10-15" @default.
- W4387569602 title "Digital Determinants of Health: Health data poverty amplifies existing health disparities—A scoping review" @default.
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- W4387569602 doi "https://doi.org/10.1371/journal.pdig.0000313" @default.
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- W4387569602 hasPublicationYear "2023" @default.
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