Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review
JMIR Medical Informatics2022Vol. 10(5), pp. e36388–e36388
Citations Over TimeTop 10% of 2022 papers
Abstract
The broad scope of medical ML applications and potential patient harms demand an increased emphasis on evaluation and mitigation of racial bias in clinical ML. However, the adoption of algorithmic fairness principles in medicine remains inconsistent and is limited by poor data availability and ML model reporting. We recommend that researchers and journal editors emphasize standardized reporting and data availability in medical ML studies to improve transparency and facilitate evaluation for racial bias.
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