Image-based artificial intelligence (AI) systems for disease detection are increasingly being developed, and it is vital that these tools are robust and effective in heterogeneous clinical settings. To date, performance has been assessed in an ad hoc manner as there are no approved guidelines for evaluation. Most studies have methodological weaknesses and results that are not comparable. A standardised and impartial framework for performance, generalisability, and suitability assessment of AI tools will address these needs and enable more efficient, reliable, and reproducible validation of image-based AI systems for disease detection. This project will use breast cancer screening as the exemplar to inform the design of such a framework.