A multinational team of researchers has identified potential pitfalls in the evaluation and measurement of algorithmic bias in AI radiology models. They suggest best practices and future directions to mitigate bias in three key areas: medical image datasets, demographic definitions, and statistical evaluations of bias. The team emphasizes the importance of accurate measurements of race- and/or ethnicity-based biases in AI models to prevent health policies being established in error.
Forecast for 6 months: In the next 6 months, we can expect to see increased awareness and discussion about the importance of mitigating bias in AI radiology models. Researchers and developers will begin to implement best practices and standards for reporting demographics and dataset characteristics.
Forecast for 1 year: Within the next year, we can expect to see the development of new AI radiology models that incorporate more diverse and representative datasets. Additionally, there will be a growing emphasis on establishing consensus on the definition of bias and developing universal fairness metrics.
Forecast for 5 years: In the next 5 years, AI radiology will become increasingly integrated into clinical practice, with a focus on reducing bias and improving accuracy. We can expect to see the development of new technologies, such as generative AI, that can create synthetic imaging datasets with more balanced representation of demographic and confounding variables.
Forecast for 10 years: In the next 10 years, AI radiology will have transformed the field of radiology, with AI-driven software becoming the norm. We can expect to see significant improvements in accuracy and reduced bias, leading to better patient outcomes and more equitable healthcare.
