Effect of Uncertainty-Aware AI Models on Pharmacists’ Reaction Time and Decision-Making in a Web-Based Mock Medication Verification Task: Randomized Controlled Trial
Corey A. Lester; Brigid Rowell; Yifan Zheng; Zoe Co; Vincent D. Marshall; Jin Yong Kim; Qiyuan Chen; Raed Al Kontar · 2025 · JMIR Medical Informatics
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Background: Artificial intelligence (AI)-based clinical decision support systems are increasingly used in health care. Uncertainty-aware AI presents the model's confidence in its decision alongside its prediction, whereas black-box AI only provides a prediction. Little is known about how this type of AI affects health care providers' work performance and reaction time. Objective: This study aimed to determine the effects of black-box and uncertainty-aware AI advice on pharmacist decision-making and reaction time. Methods: Recruitment emails were sent to pharmacists through professional listser
Abstract by Corey A. Lester; Brigid Rowell; Yifan Zheng; Zoe Co; Vincent D. Marshall; Jin Yong Kim; Qiyuan Chen; Raed Al Kontar, JMIR Medical Informatics (2025) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.2196/64902