Integrating humans and artificial intelligence in diagnostic tasks

Abstract

This dissertation investigates the integration of humans and artificial intelligence (AI) in diagnostic tasks, focusing on user experience and interaction in explainable AI (XAI) systems. Central to this research is the development of the Subjective Information Processing Awareness (SIPA) concept, which deal with user experience in automated information processing. The work addresses the increasing reliance on AI for automating information processing in critical domains such as healthcare, where transparency and human oversight may be enabled through explainable systems. Drawing on theories of human-automation interaction, this research develops and validates a model of integrated human-AI information processing. Four empirical studies explore automation-related user experience in different contexts: digital contact tracing, automated insulin delivery, AI-supported pattern recognition, and AI-based diagnosis. The findings highlight the psychological impacts of AI explanations on trust, situation awareness, and decision-making. Based on empirical findings, this dissertation discusses the concept of diagnosticity as a central metric for successful human-AI integration and proposes a framework for designing XAI systems that enhance user experience by aligning with human information processing. The dissertation concludes with practical guidelines for developing human-centered AI systems, emphasizing the importance of SIPA, user awareness, system transparency, and maintaining human control in automated diagnostic processes.