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Explainability Design Patterns in Clinical Decision Support Systems.

Naiseh, M., 2020. Explainability Design Patterns in Clinical Decision Support Systems. In: The 14th International Conference on Research Challenges in Information Science. Proceedings, 23-25 September 2020, Limassol, Cyprus (Online), 613-620.

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Doctoral.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.


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DOI: 10.1007/978-3-030-50316-1


This paper reports on the ongoing PhD project in the field of explaining the clinical decision support systems (CDSSs) recommendations to medical practitioners. Recently, the explainability research in the medical domain has witnessed a surge of advances with a focus on two main methods: The first focuses on developing models that are explainable and transparent in its nature (e.g. rule-based algorithms). The second investigates the interpretability of the black-box models without looking at the mechanism behind it (e.g. LIME) as a post-hoc explanation. However, overlooking the human-factors and the usability aspect of the explanation introduced new risks following the system recommendations, e.g. over-trust and under-trust. Due to such limitation, there is a growing demand for usable explanations for CDSSs to enable the integration of trust calibration and informed decision-making in these systems by identifying when the recommendation is correct to follow. This research aims to develop explainability design patterns with the aim of calibrating medical practitioners trust in the CDSSs. This paper concludes the PhD methodology and literature around the research problem is also discussed.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Explainability; Decision support systems; User-Centred Design; Trust
Group:Faculty of Science & Technology
ID Code:34804
Deposited By: Symplectic RT2
Deposited On:12 Nov 2020 14:38
Last Modified:14 Mar 2022 14:25


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