AI adoption by human experts: Evidence from primary care physicians

Shan Huang, Renke Schmacker and Hannes Ullrich

No 26199, RFBerlin Discussion Paper Series from ROCKWOOL Foundation Berlin (RFBerlin)

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 as 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 largely ignore the AI tool; linked administrative data show that these non-adopters exhibit lower technology use at their clinics, but that they are similar to adopters in clinical practice and prescribing measures. 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 in our setting. Our findings highlight the importance of training and information design for AI implementation.

Keywords: expert decision-making; artificial intelligence; healthcare; mental models (search for similar items in EconPapers)
JEL-codes: D81 D83 I11 J24 O33 (search for similar items in EconPapers)
Date: 2026-07
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