Eye tracking insights into physician behaviour with safe and unsafe explainable AI recommendations.

ABSTRACT: 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 physicians with context for the AI suggestion and thereby helping them to reject unsafe AI recommendations). Here, we used objective neurobehavioural measures (eye-tracking) to see how physicians respond to XAI with N = 19 ICU physicians in a hospital's clinical simulation suite. Prescription decisions were made both pre- and post-reveal of either a safe or unsafe AI recommendation and four different types of simultaneously presented XAI. We used overt visual attention as a marker for where physician mental attention was directed during the simulations. Unsafe AI recommendations attracted significantly greater attentio

SUBMITTER: Nagendran M 

PROVIDER: S-EPMC11297294 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature