AI Adoption by Human Experts: Evidence from Primary Care Physicians

Abstract

AI can raise productivity by extracting information from rich data, yet little is known about how experts weigh AI-generated signals against established decision-support tools. We conduct a nationwide survey experiment with 372 Danish primary care physicians (21.5% of all clinics), who make diagnostic and treatment decisions on urinary tract infection vignettes before and after receiving a diagnostic signal. Holding accuracy constant, we randomize between-subjects whether the signal appears as an AI prediction or a commonly used dipstick test result. Physicians update beliefs 41% less in response to AI than to dipstick signals, consistent with AI skepticism. Roughly one-third of physicians ignore the AI tool; linked administrative data show that these non-adopters resemble adopters on a range of observables, including clinical practice and prescribing measures, except for lower baseline technology use at their clinics. When physicians use the AI tool, they ignore asymmetry in informativeness between positive and negative signals and, when shown both the AI and a redundant signal, exhibit correlation neglect. These frictions in information processing lead to increased antibiotic prescribing with the AI signal. Our findings highlight the importance of training and information design for AI implementation.