Quantifying the impact of AI recommendations with explanations on prescription decision making.

ABSTRACT: The influence of AI recommendations on physician behaviour remains poorly characterised. We assess how clinicians' decisions may be influenced by additional information more broadly, and how this influence can be modified by either the source of the information (human peers or AI) and the presence or absence of an AI explanation (XAI, here using simple feature importance). We used a modified between-subjects design where intensive care doctors (N = 86) were presented on a computer for each of 16 trials with a patient case and prompted to prescribe continuous values for two drugs. We used a multi-factorial experimental design with four arms, where each clinician experienced all four arms on different subsets of our 24 patients. The four arms were (i) baseline (control), (ii) peer human clin

SUBMITTER: Nagendran M 

PROVIDER: S-EPMC10630476 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature