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Effect of Uncertainty-Aware AI Models on Pharmacists’ Reaction Time and Decision-Making in a Web-Based Mock Medication Verification Task: Randomized Controlled Trial

Effect of Uncertainty-Aware AI Models on Pharmacists’ Reaction Time and Decision-Making in a Web-Based Mock Medication Verification Task: Randomized Controlled Trial

The effects of AI assistance on user trust are reported by Kim et al [28] In summary, pharmacists’ trust varied by AI help type and the verification outcome. Overall, uncertainty-aware AI significantly increased pharmacists’ trust. Pharmacists made the correct decision 91.3%, 93.1%, and 94.2% across all trials in the no-help, uncertainty-aware AI, and black-box AI blocks, respectively (χ22=12.5, P value=.001).

Corey Lester, Brigid Rowell, Yifan Zheng, Zoe Co, Vincent Marshall, Jin Yong Kim, Qiyuan Chen, Raed Kontar, X Jessie Yang

JMIR Med Inform 2025;13:e64902

Artificial Intelligence-Driven Biological Age Prediction Model Using Comprehensive Health Checkup Data: Development and Validation Study

Artificial Intelligence-Driven Biological Age Prediction Model Using Comprehensive Health Checkup Data: Development and Validation Study

The initial baseline data were used, and the participants included a total of 81,211 Koreans, who comprised the Health and Prevention Enhancement (H-PEACE) cohort. The details of the H-PEACE cohort have been described previously [16]. To summarize, each participant completed a questionnaire on their past medical history and underwent anthropometric measurements and laboratory tests after at least 10 hours of fasting on the same day.

Chang-Uk Jeong, Jacob S Leiby, Dokyoon Kim, Eun Kyung Choe

JMIR Aging 2025;8:e64473