Types of bias through medical AI development stages, where they occur, their potential clinical consequences, and mitigation techniques.

Types of bias through medical AI development stages, where they occur, their potential clinical consequences, and mitigation techniques.

<p>Types of bias through medical AI development stages, where they occur, their potential clinical consequences, and mitigation techniques.</p>

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Bibliographic Details
Main Author:	James L. Cross (14045227) (author)
Other Authors:	Michael A. Choma (3952322) (author), John A. Onofrey (11778224) (author)
Published:	2024
Subjects:	Genetics Neuroscience Biotechnology Ecology Cancer Science Policy Hematology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified statistical debiasing methods patients benefit equitably missing patient findings longstanding healthcare disparities insufficient sample sizes end users interact differentially across subgroups demonstrate unbiased application clinically unmeaningful predictions certain patient groups medical artificial intelligence substandard clinical decisions significant clinical consequences model &# 8217 xlink "> biases discuss potential biases affect ai algorithms standardized bias reporting mitigate bias must nonrandomly missing data diverse data sets substandard care practices biased medical ai involve clinical decision ai development pipeline thorough model evaluation making </ p medical ai clinical decision model development clinical utility clinical trials clinical settings model performance model interpretability ai lifecycle introduce bias data outside data features world implementation transparency requirements training cohort suboptimal performance social determinants rigorous validation previous validation performance metrics left unaddressed including capturable easily captured different stages diagnosis codes compound throughout algorithm underestimation
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