The ethical adoption of artificial intelligence in radiology

Abstract

Artificial intelligence (AI) is rapidly transforming healthcare-with radiology at the pioneering forefront. To be trustfully adopted, AI needs to be lawful, ethical and robust. This article covers the different aspects of a safe and sustainable deployment of AI in radiology during: <i>training, integration and regulation</i>. For training, data must be appropriately valued, and deals with AI companies must be centralized. Companies must clearly define anonymization and consent, and patients must be well-informed about their data usage. Data fed into algorithms must be made AI-ready by refining, purification, digitization and centralization. Finally, data must represent various demographics. AI needs to be safely integrated with <i>radiologists-in-the-loop:</i> guiding forming concepts of AI solutions and supervising training and feedback. To be well-regulated, AI systems must be approved by a health authority and agreements must be made upon liability for errors, roles of supervised and unsupervised AI and fair workforce distribution (between AI and radiologists), with a renewal of policy at regular intervals. Any errors made must have a root-cause analysis, with outcomes fedback to companies to close the loop<i>-</i>thus enabling a dynamic best prediction system. In the distant future, AI may act autonomously with little human supervision. Ethical training and integration can ensure a "transparent" technology that will allow insight: helping us reflect on our current understanding of imaging interpretation and fill knowledge gaps, eventually moulding radiological practice. This article proposes recommendations for ethical practise that can guide a nationalized framework to build a <i>sustainable and transparent</i> system.

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