Deep learning in radiology: ethics of data and on the value of algorithm transparency, interpretability and explainability

Abstract

AI systems are quickly being adopted in radiology and, in general, in healthcare. A myriad of systems is being proposed and developed on a daily basis for high-stake decisions that can lead to unwelcome and negative consequences. AI systems trained under the supervised learning paradigm greatly depend on the quality and amount of data used to develop them. Nevertheless, barriers in data collection and sharing limit the data accessibility and potential ethical challenges might arise due to them leading, for instance, to systems that do not offer equity in their decisions and discriminate against certain patient populations or that are vulnerable to appropriation of intellectual property, among others. This paper provides an overview of some of the ethical issues both researchers and end-users might meet during data collection and development of AI systems, as well an introduction to the current state of transparency, interpretability and explainability of the systems in radiology applications. Furthermore, we aim to provide a comprehensive summary of currently open questions and identify key issues during the development and deployment of AI systems in healthcare, with a particular focus on the radiology area.

Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer’s disease?

“…Additionally, even if the patient did not receive some compensation, the question remains whether the patient should have control or be informed of where their health information is transferred. There is no consensus regarding data ownership in these potential situations (Fernandez-Quilez, 2023). Additionally, large companies have great interest in the USD 8.3 trillion-dollar healthcare industry (Thomason, 2021).…”

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“…8 In some places, patients control how their sensitive health data are re-used. 9 10 In others, this control is superseded by the potential to benefit the society at large, or, in the case of radiological data, ownership may even belong to the entity that conducted the imaging. 10…”

“…5 For example, if socioeconomically disadvantaged patients tend to do worse when receiving treatment compared with the overall population, AI algorithms may recommend against treating them. 9 Integrating AI systems in clinical workflows also requires the initial capital to invest in such a system and local expertise to use it. Like other technological advances, this is likely to occur more readily in urban and affluent communities that may further widen inequalities in the care people receive.…”

“…8 In some places, patients control how their sensitive health data are re-used. 9,10 In others, this control is superseded by the potential to benefit the society at large, or, in the case of radiological data, ownership may even belong to the entity that conducted the imaging. 10 It can be argued that all participants within healthcare systems (patients, providers, institutions, and industry alike) bear some moral responsibility to improve such systems, with patients mainly contributing to this through the secondary use of their de-identified data in guiding discovery, learning, and medical development.…”

Deep Learning for Digital Pathology in Limited Data Scenarios

“…Ethical discussions can be raised regarding data collection and representation, how the algorithms should be used and how their output should be acted upon, who gets access to care, etc. [17], [81]. This thesis does not aim to give a comprehensive accounting of all ethical aspects but rather focuses on discussing one that relates closely to the core topic of this thesis: understanding the data.…”