Image-based Artificial Intelligence (AI) systems for disease detection are increasingly being developed, making necessary their effectiveness and trustworthiness in heterogeneous clinical settings, as well as their evaluation by approved guidelines. To address these points, MAIBAI aims at developing a standardized and impartial framework for the assessment of AI tool performance, generalizability and suitability in the clinical environment.
This will enable a more efficient, reliable and reproducible validation of image-based AI systems for disease detection. Using breast screening as an exemplar, AI tools will be benchmarked on a large real-world database of mammographic images, with the final goal of designing a metrological framework for AI assessment and explainability in diagnostic imaging.
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