AI models developed using data from very similar populations may not work as well for other groups, and could reflect or even worsen existing racial and ethnic disparities in health care. This study tested whether this was the case for an AI model that uses electrocardiograms (ECGs) to detect a weakened heart pumping function, known as low left ventricular ejection fraction.
The model had been developed with data from nearly 45,000 patients, 96.2% of whom were non-Hispanic White. When tested on a separate group of almost 53,000 patients, it performed consistently well across all the racial and ethnic groups analysed: non-Hispanic White, Asian, Black/African American, Hispanic/Latino and American Indian/Native Alaskan. A separate AI model was able to identify patients’ racial group from the ECG to some extent, showing that ECG characteristics do vary by race, but this did not affect the first model’s performance.
The authors conclude that, in this case, racial differences in the ECG did not affect the model’s ability to detect the condition. Even so, they recommend that the performance of all new AI tools in medicine be reported across different ethnic, racial, age and sex groups, to ensure they are used responsibly.
Learn more about this study here: https://doi.org/10.1161/CIRCEP.119.007988
Reference:
Noseworthy, P.A., Attia, Z.I., Brewer, L.C., Hayes, S.N., Yao, X., Kapa, S., Friedman, P.A., & Lopez-Jimenez, F. (2020). Assessing and Mitigating Bias in Medical Artificial Intelligence: The Effects of Race and Ethnicity on a Deep Learning Model for ECG Analysis. Circulation: Arrhythmia and Electrophysiology, 13(3), e007988.