In this reply to a ‘Letter to the editor‘, the authors seek to correct a few misunderstandings relating to equitable and inequitable biases in machine learning and radiology that were mentioned by the authors of the letter, in which topics such as cultural biases and ‘socially related’ cognitive biases were discussed, as well as how to deal with these biases. The authors do not claim that datasets used in algorithms should be ‘diversified’ for their own sake, but that there is a need to correct for biases in datasets and technologies, which can be done through better representation of disadvantaged groups to increase the overall well-being and health status of all patients.
Authors: Mirjam Pot & Barbara Prainsack
