Abstract
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.
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Acknowledgements
Support is acknowledged from EPSRC project EP/P009964/1 PAMBAYESIAN for MR and WM and for WM from the Institutional Links grant 352394702, funded by the UK Department of Business, Energy and Industrial Strategy.
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Neves, M.R., Marsh, D.W.R. (2019). Modelling the Impact of AI for Clinical Decision Support. In: Riaño, D., Wilk, S., ten Teije, A. (eds) Artificial Intelligence in Medicine. AIME 2019. Lecture Notes in Computer Science(), vol 11526. Springer, Cham. https://doi.org/10.1007/978-3-030-21642-9_37
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DOI: https://doi.org/10.1007/978-3-030-21642-9_37
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