Deep learning models trained on a dataset lacking racial diversity could hinder the detection of pathology in underrepresented minority patients.

A study presented at the Radiological Society of North America (RSNA) 2021 Annual Meeting demonstrates the importance of using racially diverse datasets while training artificial intelligence (AI) systems to ensure fair outcomes.

“As the rapid development of deep learning in medicine continues, there are concerns of potential bias when interpreting radiological images,” the authors wrote. “As future medical AI systems are approved by regulators, it is crucial that model performance on different racial/ethnic groups is shared to ensure that safe and fair systems are being implemented.”

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