Are GP Residents Taking Prior Information on AI Accuracy into Account when Receiving AI Assistance? A Randomized Controlled Experiment (Preprint)

BACKGROUND Evidence has shown that artificial intelligence can aid clinicians in the diagnosis of skin disease, but factors influencing human-AI collaboration remain underexplored. Successful human-AI collaboration relies on the alignment between a person’s reliance on the tool and its actual capabilities (i.e. trust calibration). However, little is known about which factors shape this interaction. We hypothesized that reliance on AI may be predicted by human performance expectancy and investigated if providing prior information about the statistical performance of AI tools influences the reliance on different AI tools. OBJECTIVE The influence of prior information about AI performance on AI reliance among GP residents was examined experimentally. Specifically, we assessed whether framing an AI tool as high- versus low-performing affected AI reliance, diagnostic accuracy, and confidence when diagnosing skin lesions. METHODS General practice (GP) residents diagnosed 50 clinical images of skin lesions as malignant or benign. Participants first diagnosed the lesions without AI-tool support. They then received a diagnostic suggestion, framed as coming from an AI-tool, and were asked whether they wanted to change their original diagnosis. The images were presented in two (counterbalanced) blocks: In one block, the residents were informed that the AI-tool had a lower accuracy and in the other block, the AI-tool supposedly had a higher accuracy. In reality, the suggestions were mock-ups and equally accurate in both blocks. RESULTS Eighty-three GP residents (Mdn=39 years old, 60 female, 1 non-binary) completed the study. Displaying prior information about AI performance did not affect much participants relied on AI (p=0.319). Overall classification accuracy improved after receiving assistance (OR = 1.34, SE = 0.08, 95%Cl [1.20, 1.50], p