Many AI (or ML) systems have been proposed for clinical decision support. Clinical usefulness is assessed in an ‘Impact Study’, a form of trial of a completed system. In development, in contrast, the focus is on AI accuracy measures, such as the AUC. To improve impact and to justify the cost of a study, the impact of a proposed AI system should be modelled during its development. We show that an Influence Diagram can be used for this and provide a small set of generic models for diagnostic AI systems. We show that how the AI interacts with clinical decision makers is at least as important as its predictive accuracy.
CITATION STYLE
Neves, M. R., & Marsh, D. W. R. (2019). Modelling the impact of AI for clinical decision support. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11526 LNAI, pp. 292–297). Springer Verlag. https://doi.org/10.1007/978-3-030-21642-9_37
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