This session explores the critical aspects of sustainability and equity in radiology AI. It aims to provide insights into the carbon footprint of training and deploying AI models in clinical settings, as well as strategies for reducing their environmental impact. The session also addresses the crucial issues of equity and bias in radiology AI, emphasising the need for fair and unbiased AI systems, and includes a patient's perspective on the use of AI in radiology.
Chairperson's introduction
Daniel Truhn, Aachen / Germany
Sustainable AI infrastructures: key considerations for radiology departments
Sophie Thornander, Amsterdam / Netherlands
How green is clinical AI? The carbon impact of AI in clinical routine
Marta Ligero Hernández, Dresden / Germany
Equity and bias of AI in radiology
Judy Gichoya, indianapolis / United States
Did AI report my scan: a patient's perspective
Erik Briers, Hasselt / Belgium
Panel discussion: Who is responsible for managing the carbon footprint and bias in imaging AI?