ArticleJMIR human factors2025
The Effects of Presenting AI Uncertainty Information on Pharmacists' Trust in Automated Pill Recognition Technology: Exploratory Mixed Subjects Study.
Article in JMIR human factors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Effect of Uncertainty-Aware AI Models on Pharmacists' Reaction Time and Decision-Making in a Web-Based Mock Medication Verification Task: Randomized Controlled Trial.JMIR medical informatics · 2025Trial
- Beyond the Dispensary: Pharmacists' Perspectives on the Responsible Implementation of Emerging Clinical, Digital, and Ethically Sensitive Roles.Pharmacy (Basel, Switzerland) · 2026Article
- Effects of AI Assistance Timing on Pharmacists' Trust in Automated Pill Recognition Technology: Within-Participants Experimental Study.JMIR human factors · 2026Article
- Beyond Binary Decisions: Evaluating the Effects of AI Error Type on Trust and Performance in AI-Assisted Tasks.Human factors · 2025Article
- Pharmacists' propensity to trust automated technologies: A demographic analysis.Journal of the American Pharmacists Association : JAPhAArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
backgroundDispensing errors significantly contribute to adverse drug events, resulting in substantial health care costs and patient harm. Automated pill verification technologies have been developed to aid pharmacists with medication dispensing. However, pharmacists' trust in such automated technologies remains unexplored.
objectiveThis study aims to investigate pharmacists' trust in automated pill verification technology designed to support medication dispensing.
methodsThirty licensed pharmacists in the United States performed a web-based simulated pill verification task to determine whether an image of a filled medication bottle matched a known reference image. Participants completed a block of 100 verification trials without any help, and another block of 100 trials with the help of an imperfect artificial intelligence (AI) aid recommending acceptance or rejection of a filled medication bottle. The experiment used a mixed subjects design. The between-subjects factor was the AI aid type, with or without an AI uncertainty plot. The within-subjects factor was the four potential verification outcomes: (1) the AI rejects the incorrect drug, (2) the AI rejects the correct drug, (3) the AI approves the incorrect drug, and (4) the AI approves the correct drug. Participants' trust in the AI system was measured. Mixed model (generalized linear models) tests were conducted with 2-tailed t tests to compare the means between the 2 AI aid types for each verification outcome.
resultsParticipants had an average trust propensity score of 72 (SD 18.08) out of 100, indicating a positive attitude toward trusting automated technologies. The introduction of an uncertainty plot to the AI aid significantly enhanced pharmacists' end trust (t
conclusionsTo the best of our knowledge, this study is the first attempt to examine pharmacists' trust in automated pill verification technology. Our findings reveal that pharmacists have a favorable disposition toward trusting automation. Moreover, providing uncertainty information about the AI's recommendation significantly boosts pharmacists' trust in AI aid, highlighting the importance of developing transparent AI systems within health care.
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