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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wyatt, J.C., Altman, D.G.: Commentary: prognostic models: clinically useful or quickly forgotten? BMJ 311(7019), 1539–1541 (1995)
Altman, D.G., Vergouwe, Y., Royston, P., Moons, K.G.: Prognosis and prognostic research: validating a prognostic model. BMJ 338, b605 (2009)
Wallace, E., et al.: Framework for the impact analysis and implementation of Clinical Prediction Rules (CPRs). BMC Med. Inform. Decis. Mak. 11(1), 62 (2011)
Huang, C., et al.: Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: a retrospective cohort study. PLOS Med. 15(11) (2018)
Hae, H., et al.: Machine learning assessment of myocardial ischemia using angiography: development and retrospective validation. PLOS Med. 15(11), e1002693 (2018)
Yet, B., Neil, M., Fenton, N., Constantinou, A., Dementiev, E.: An improved method for solving hybrid influence diagrams. Int. J. Approx. Reason. 95, 93–112 (2018)
Elmore, J.G., et al.: Evaluation of 12 strategies for obtaining second opinions to improve interpretation of breast histopathology: simulation study. BMJ 353, i3069 (2016)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-21642-9_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-21641-2
Online ISBN: 978-3-030-21642-9
eBook Packages: Computer ScienceComputer Science (R0)