  Biased information (recently termed bisinformation) continues to be taught in
medical curricula, often long after having been debunked. In this paper, we
introduce BRICC, a firstin-class initiative that seeks to mitigate medical
bisinformation using machine learning to systematically identify and flag text
with potential biases, for subsequent review in an expert-in-the-loop fashion,
thus greatly accelerating an otherwise labor-intensive process. A gold-standard
BRICC dataset was developed throughout several years, and contains over 12K
pages of instructional materials. Medical experts meticulously annotated these
documents for bias according to comprehensive coding guidelines, emphasizing
gender, sex, age, geography, ethnicity, and race. Using this labeled dataset,
we trained, validated, and tested medical bias classifiers. We test three
classifier approaches: a binary type-specific classifier, a general bias
classifier; an ensemble combining bias type-specific classifiers
independently-trained; and a multitask learning (MTL) model tasked with
predicting both general and type-specific biases. While MTL led to some
improvement on race bias detection in terms of F1-score, it did not outperform
binary classifiers trained specifically on each task. On general bias
detection, the binary classifier achieves up to 0.923 of AUC, a 27.8%
improvement over the baseline. This work lays the foundations for debiasing
medical curricula by exploring a novel dataset and evaluating different
training model strategies. Hence, it offers new pathways for more nuanced and
effective mitigation of bisinformation.
