Demographic bias in misdiagnosis by computational pathology models

Practical Application of Deep Learning in Diagnostic Neuropathology—Reimagining a Histological Asset in the Era of Precision Medicine

“…Another important priority is the use of demographically inclusive datasets across key variables such as age, sex, gender, and ethnicity during model training, as imbalanced medical imaging training datasets have been shown to lead to models that perform worse in underrepresented groups [ 64 , 65 ]. For instance, a recent study using large public datasets from The Cancer Genome Atlas and the EBRAINS brain tumor atlas, comprised primarily of tumors from white patients, showed a 16.0% performance gap in the prediction of IDH1 status in gliomas from black vs. white patients [ 65 ].…”

“…Another important priority is the use of demographically inclusive datasets across key variables such as age, sex, gender, and ethnicity during model training, as imbalanced medical imaging training datasets have been shown to lead to models that perform worse in underrepresented groups [ 64 , 65 ]. For instance, a recent study using large public datasets from The Cancer Genome Atlas and the EBRAINS brain tumor atlas, comprised primarily of tumors from white patients, showed a 16.0% performance gap in the prediction of IDH1 status in gliomas from black vs. white patients [ 65 ]. In addition to highlighting the need for bias mitigation strategies, including external validation with existing, prospective, and demographic-stratified datasets, the authors demonstrate the utility of novel strategies such as self-supervised vision foundation models to improve model generalizability.…”