Mitigating Discontinuance in Medical AI Systems: The Role of AI Explanations

Aycan Aslan (), Maike Greve () and Lutz Kolbe ()
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Aycan Aslan: University of Goettingen, Chair of Information Management
Maike Greve: University of Goettingen, Chair of Information Management
Lutz Kolbe: University of Goettingen, Chair of Information Management

A chapter in Artificial Intelligence, Data, and Decision-Making, 2026, pp 69-83 from Springer

Abstract: Abstract Despite significant advancements in medical artificial intelligence (AI) systems, these technologies are prone to mistake in their predictions. These mistakes can significantly affect medical experts’ willingness to continue using these systems. To mitigate potential discontinuation, existing research indicates that providing additional information alongside predictions, can lessen negative outcomes like discontinuation. Given the potential impact on users’ information processing, we hypothesize that AI explanations, detailing the system's decision-making process, can also influence the likelihood of discontinuing use after an AI mistake. Through an online experiment with medical experts (n = 227), we demonstrate that such explanations can influence medical experts’ information processing and, consequently, mitigate the adverse effects on the actual discontinuation of AI systems following a mistake.

Keywords: Artificial intelligence; Decision-making; Explainability; Discontinuance; Medicine (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-08480-4_5

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