AI trustworthiness in prostate cancer imaging: a look at algorithmic and system transparency^*

Abstract

A responsible approach to artificial intelligence and machine learning technologies, grounded in sound scientific foundations, technical robustness, rigorous testing and validation, risk-based continuous monitoring and alignment with human values is imperative to guarantee their favorable impact and prevent any adverse effects they may have on individuals and communities. An essential aspect of responsible development is transparency, which constitutes a fundamental principle of the European approach towards artificial intelligence. Transparency can be achieved at different levels, such as data origin and use, system development, operation and usage. In this paper, we present the techniques implemented and delivered in the EU H2020 ProCAncer-I project to meet the transparency requirements at the different levels required.Clinical Relevance: This paper examines the primary transparency hurdles in artificial intelligence for medical imaging diagnostics, and presents the approaches that the EU H2020 project ProCAncer-I is taking to address them.

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