Technical robustness and bias in medical imaging

Technical robustness refers to the capacity of AI systems to be adverse to risks and to behave reliably, minimising and preventing unintentional and unexpected harm. This talk will cover three of the key points that robust AI systems should address: 1) safety and fall-back plans; 2) accuracy; and 3) reliability and reproducibility. We will discuss to which extent these are being addressed in current medical imaging applications, and conclude with an overview of the effect of bias on a system’s robustness, and related mitigation strategies.