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On the influence of explainable AI and time pressure on confirmation bias in digital pathology
(2024)
The rapid development of artificial intelligence (AI) in recent years has opened the door to its employment for life-saving applications in the medical field. In particular, the increasing prevalence of AI in diagnostic pathology holds the potential to enhance diagnostic accuracy and provide more efficient patient care. While explainability is often identified as a crucial component in terms of safety and AI acceptance for future clinical use, it may also introduce or strengthen human cognitive biases such as the confirmation bias.
Confirmation Bias describes the selective search for and intake of information to strengthen prior existing beliefs, which could be further amplified by the time pressure ubiquitously present in everyday pathology. To date, very few user-centric studies have been carried out in this domain, thus this thesis seeks to empirically examine the effects of explainable AI and time pressure on confirmation bias during the interaction with clinical decision support systems in a mixed-method study and aims to contribute to the safe and efficient development and design of intelligent support systems in the medical sector.