Ethical Considerations and Fairness in the Use of Artificial Intelligence for Neuroradiology

Abstract

In this review, concepts of algorithmic bias and fairness are defined qualitatively and mathematically. Illustrative examples are given of what can go wrong when unintended bias or unfairness in algorithmic development occurs. The importance of explainability, accountability, and transparency with respect to artificial intelligence algorithm development and clinical deployment is discussed. These are grounded in the concept of "primum no nocere" (first, do no harm). Steps to mitigate unfairness and bias in task definition, data collection, model definition, training, testing, deployment, and feedback are provided. Discussions on the implementation of fairness criteria that maximize benefit and minimize unfairness and harm to neuroradiology patients will be provided, including suggestions for neuroradiologists to consider as artificial intelligence algorithms gain acceptance into neuroradiology practice and become incorporated into routine clinical workflow. ABBREVIATION: AI ¼ artificial intelligenceA rtificial intelligence (AI) is beginning to transform the practice of radiology, from order entry through image acquisition and reconstruction, workflow management, diagnosis, and treatment decisions. AI will certainly change neuroradiology practice across routine workflow, education, and research. Neuroradiologists are understandably concerned about how AI will affect their subspecialty and how they can shape its development. Multiple published consensus statements advocate the need for radiologists to play a primary role in ensuring that AI software used for clinical care is fair to and unbiased against specific groups of patients. 1 In this review, we focus on the need for developing and implementing fairness criteria and how to balance competing interests that minimize harm and maximize patient benefits when implementing AI solutions in neuroradiology. The responsibility for promoting health care equity rests with the entire neuroradiology community, from academic leaders to private practitioners. We all have a stake in establishing best practices as AI enters routine clinical practice. Definitions"Ethics," in a strict dictionary definition, is a theory or system of values that governs the conduct of individuals and groups. 2 Ethical physicians should endeavor to promote fairness and avoid bias in their personal treatment of patients and with respect to the health care system at large. A biased object yields 1 outcome more frequently than statistically expected, eg, a 2-headed coin. Similarly, a biased algorithm systematically produces outcomes that are not statistically expected. One proposed definition for algorithmic bias in health care systems is "when the application of an algorithm compounds existing inequities in socioeconomic status, race, ethnic background, religion, sex, disability, or sexual orientation to amplify them and adversely impact inequities in health systems." 3 This definition, while not ideal, is a request for developers and end users of AI algorithms in health care to be aware of the poten...

Gross failure rates and failure modes for a commercial AI‐based auto‐segmentation algorithm in head and neck cancer patients

“…In terms of the other failure modes identified, patient setup position and non‐standard anatomy, it is likely that such failures are caused by the absence of sufficient numbers of these “unusual” case types in the model training data set of the AI‐based auto‐segmentation software. This sort of dataset bias in AI is a well‐known 29–34 and should be expected, given the relative frequency of such cases in the clinic. In addition, due to the observed wide anatomical variation in patients with non‐standard anatomy, it may not be possible to include sufficient numbers of this patient type in a model training data set for the training to be sufficiently effective due to the large patient dataset numbers typically required to train DL models 35 …”