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Giving DIAnA More TIME – Guidance for the Design of XAI-Based Medical Decision Support Systems

Enrico Bunde; Daniel Eisenhardt; Daniel Sonntag; Hans-Jürgen Profitlich; Christian Meske
In: 18th International Conference on Design Science Research in Information Systems and Technology, DESRIST 2023. International Conference on Design Science Research in Information Systems and Technology (DESRIST-2023), May 31 - June 3, Pretoria, South Africa, Springer Nature Switzerland, 5/2023.


Future healthcare ecosystems integrating human-centered artificial intelligence (AI) will be indispensable. AI-based healthcare technologies can sup- port diagnosis processes and make healthcare more accessible globally. In this con- text, we conducted a design science research project intending to introduce design principles for user interfaces (UIs) of explainable AI-based (XAI) medical deci- sion support systems (XAI-based MDSS). We used an archaeological approach to analyze the UI of an existing web-based system in the context of skin lesion classification called DIAnA (Dermatological Images – Analysis and Archiving). One of DIAnA’s unique characteristics is that it should be usable for the stake- holder groups of physicians and patients. We conducted the in-situ analysis with these stakeholders using the think-aloud method and semi-structured interviews. We anchored our interview guide in concepts of the Theory of Interactive Media Effects (TIME), which formulates UI features as causes and user psychology as effects. Based on the results, we derived 20 design requirements and developed nine design principles grounded in TIME for this class of XAI-based MDSS, either associated with the needs of physicians, patients, or both. Regarding evaluation, we first conducted semi-structured interviews with software developers to assess the reusability of our design principles. Afterward, we conducted a survey with user experience/interface designers. The evaluation uncovered that 77% of the participants would adopt the design principles, and 82% would recommend them to colleagues for a suitable project. The findings prove the reusability of the design principles and highlight a positive perception by potential implementers.

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