Not all biases are bad: equitable and inequitable biases in machine learning and radiology

Abstract

The application of machine learning (ML) technologies in medicine generally but also in radiology more specifically is hoped to improve clinical processes and the provision of healthcare. A central motivation in this regard is to advance patient treatment by reducing human error and increasing the accuracy of prognosis, diagnosis and therapy decisions. There is, however, also increasing awareness about bias in ML technologies and its potentially harmful consequences. Biases refer to systematic distortions of datasets, algorithms, or human decision making. These systematic distortions are understood to have negative effects on the quality of an outcome in terms of accuracy, fairness, or transparency. But biases are not only a technical problem that requires a technical solution. Because they often also have a social dimension, the ‘distorted’ outcomes they yield often have implications for equity. This paper assesses different types of biases that can emerge within applications of ML in radiology, and discusses in what cases such biases are problematic. Drawing upon theories of equity in healthcare, we argue that while some biases are harmful and should be acted upon, others might be unproblematic and even desirable—exactly because they can contribute to overcome inequities.

Artificial Intelligence for Radiological Paediatric Fracture Assessment: A Systematic Review

“…osteogenesis imperfecta). This could be due to fewer children within these categories attending emergency departments to provide the necessary imaging data for training AI models, but the result is that speci c paediatric populations may be unintentionally marginalised or poorly served by such new technologies and raises potential ethical considerations about their future usage particularly when performance characteristics are extrapolated beyond the population on which the tool was developed and validated [54]. An example would be an AI tool which could help to evaluate the particular aspects of fractures relating to suspected physical abuse as an adjunct to clinical practice given that many practising paediatric radiologists do not feel appropriately trained or con dent in this aspect of imaging assessment [55][56][57][58].…”

Letter to the editor: “Not all biases are bad: equitable and inequitable biases in machine learning and radiology”

“…Author details 1 Centre Antoine Lacassagne, 33 Avenue de Valombrose, 06100 Nice, France. 2 Median Technologies, 1800 route des crêtes, 06560 Valbonne, France.…”

“…Dear Editor in Chief, We read with interest the article entitled 'Not all biases are bad: equitable and inequitable biases in machine learning and radiology' by Pot et al [1] recently published in Insights into Imaging.…”

Reply to Letter to the Editor on “Not all biases are bad: equitable and inequitable biases in machine learning and radiology”

“…Iannessi et al [ 1 ] commented on our paper “Not all biases are bad: equitable and inequitable biases in machine learning and radiology” [ 2 ]. We thank the authors for their critique.…”