Medical healthcare has advanced substantially due to advancements in Artificial Intelligence (AI) techniques for early disease detection alongside support for clinical decisions. However, a gap exists in widespread adoption of results of these algori...
A key factor in successful human-robot interaction (HRI) is the predictability of a robot's actions. Visual cues, such as eyes or arrows, can serve as directional indicators to enhance predictability, potentially improving performance and increasing ...
AI has propelled the potential for moving toward personalized health and early prediction of diseases. Unfortunately, a significant limitation of many of these deep learning models is that they are not interpretable, restricting their clinical utilit...
Despite AI tools hold significant potential to enhance English language teaching in primary education, their sustained adoption by teachers remains inconsistent. A key gap in current research is the lack of understanding of how psychological factors ...
BACKGROUND: Parkinson disease (PD) is the fastest-growing neurodegenerative disorder in the world, with prevalence expected to exceed 12 million by 2040, which poses significant health care and societal challenges. Artificial intelligence (AI) system...
BACKGROUND: The use of conversational agents (CAs) in mental health therapy is gaining traction due to their accessibility, anonymity, and nonjudgmental nature. However, understanding the psychological factors driving preferences for CA-based therapy...
The objective of explainable artificial intelligence systems designed for clinical decision support (XAI-CDSS) is to enhance physicians' diagnostic performance, confidence and trust through the implementation of interpretable methods, thus providing ...
Explainable AI has garnered significant traction in science communication research. Prior empirical studies have firmly established that explainable AI communication could improve trust in AI and that trust in AI engineers was argued to be an under-e...
Human-AI collaborative innovation relies on effective and clearly defined role allocation, yet empirical research in this area remains limited. To address this gap, we construct a cognitive taxonomy trust in AI framework to describe and explain its i...
The purpose of this experiment was to investigate the effect of robot arm size, movement speed, and degrees of freedom on perceived safety, trust, mental workload, human behaviors, and task performance in a collaborative pick-and-place task. Fifty-si...
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