Quantifying the impact of AI recommendations with explanations on prescription decision making
The influence of AI recommendations on physician behaviour remains poorly characterised.
We assess how clinicians' decisions may be influenced by additional information more …
We assess how clinicians' decisions may be influenced by additional information more …
Explanation matters: An experimental study on explainable AI
P Hamm, M Klesel, P Coberger, HF Wittmann - Electronic Markets, 2023 - Springer
Explainable artificial intelligence (XAI) is an important advance in the field of machine
learning to shed light on black box algorithms and thus a promising approach to improving …
learning to shed light on black box algorithms and thus a promising approach to improving …
[HTML][HTML] Assuring the safety of AI-based clinical decision support systems: a case study of the AI Clinician for sepsis treatment
Objectives Establishing confidence in the safety of Artificial Intelligence (AI)-based clinical
decision support systems is important prior to clinical deployment and regulatory approval …
decision support systems is important prior to clinical deployment and regulatory approval …
Eye tracking insights into physician behaviour with safe and unsafe explainable AI recommendations
We studied clinical AI-supported decision-making as an example of a high-stakes setting in
which explainable AI (XAI) has been proposed as useful (by theoretically providing …
which explainable AI (XAI) has been proposed as useful (by theoretically providing …
Uncertainty in XAI: Human Perception and Modeling Approaches
Artificial Intelligence (AI) plays an increasingly integral role in decision-making processes. In
order to foster trust in AI predictions, many approaches towards explainable AI (XAI) have …
order to foster trust in AI predictions, many approaches towards explainable AI (XAI) have …
Quantifying the impact of AI recommendations with explanations on prescription decision making: an interactive vignette study
Background: The challenge of responsibly guiding clinicians to incorporate AI
recommendations and explanations into their day-to-day practice has thus far neglected the …
recommendations and explanations into their day-to-day practice has thus far neglected the …
ATIAS: A Model for Understanding Intentions to Use AI Technology
The interdisciplinary quantitative research method presented in this chapter is used to
investigate people's trust in, and intention to use, AI systems. ATIAS (AI Trust and Intention to …
investigate people's trust in, and intention to use, AI systems. ATIAS (AI Trust and Intention to …
Eye-tracking of clinician behaviour with explainable AI decision support: a high-fidelity simulation study
Explainable AI (XAI) is seen as important for AI-driven clinical decision support tools but
most XAI has been evaluated on non-expert populations for proxy tasks and in low-fidelity …
most XAI has been evaluated on non-expert populations for proxy tasks and in low-fidelity …
Evaluating the impact of explainable RL on physician decision-making in high-fidelity simulations: insights from eye-tracking metrics
Explainable reinforcement learning (XRL) is crucial for reinforcement learning (RL)
algorithms within clinical decision support systems. However, most XRL evaluations have …
algorithms within clinical decision support systems. However, most XRL evaluations have …
Interaction of doctors with explainable RL decision support via behavioural readouts of eye-tracking
Explainable reinforcement learning (XRL) is crucial for reinforcement learning (RL)
algorithms within clinical decision support systems. However, most XRL evaluations have …
algorithms within clinical decision support systems. However, most XRL evaluations have …