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Explainable artificial intelligence in paediatrics: challenges for the future
Salih, Ahmed M. Menegaz, Gloria Boyle, Elaine M.
Salih, Ahmed M.
Menegaz, Gloria
Boyle, Elaine M.
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2024-12-12
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Abstract
Background
Explainable artificial intelligence (XAI) emerged to improve the transparency of machine learning models and increase understanding of how models make actions and decisions. It helps to present complex models in a more digestible form from a human perspective. However, XAI is still in the development stage and must be used carefully in sensitive domains including paediatrics, where misuse might have adverse consequences.
Objective
This commentary paper discusses concerns and challenges related to implementation and interpretation of XAI methods, with the aim of rising awareness of the main concerns regarding their adoption in paediatrics.
Methods
A comprehensive literature review was undertaken to explore the challenges of adopting XAI in paediatrics.
Results
Although XAI has several favorable outcomes, its implementation in paediatrics is prone to challenges including generalizability, trustworthiness, causality and intervention, and XAI evaluation.
Conclusion
Paediatrics is a very sensitive domain where consequences of misinterpreting AI outcomes might be very significant. XAI should be adopted carefully with focus on evaluating the outcomes primarily by including paediatricians in the loop, enriching the pipeline by injecting domain knowledge promoting a cross-fertilization perspective aiming at filling the gaps still preventing its adoption.
Citation
Salih, A.M., Menegaz, G., Pillay, T. and Boyle, E.M. (2024) Explainable artificial intelligence in paediatrics: challenges for the future. Health Science Reports, 7(12), e70271.
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Journal article
Language
en
Description
© 2024 The Authors. Published by Wiley. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1002/hsr2.70271
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2398-8835
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Sponsors
AMS and EMB acknowledge support from The Leicester City Football Club (LCFC).