Abstract
Artificial intelligence (AI) is increasingly utilized to enhance prescription verification by screening medication orders, prioritizing pharmacist review, and, in certain implementations, suppressing or deprioritizing alerts deemed low risk. While these systems may improve efficiency and the detection of prescribing risks, they also introduce challenges related to clinician reliance, accountability, and system oversight. This editorial argues that AI-enabled prescription verification may shift, in settings where algorithmic triage or alert suppression is relied upon, safety from an active clinical judgment to a passive inference based on algorithmic silence, redistributing rather than eliminating medication safety risk. As a result, safety work transitions from preventing individual errors to maintaining vigilance through continuous monitoring and governance. Key issues discussed include automation bias, data drift and dataset shift, distributed clinical responsibility, and the limitations of traditional validation approaches, such as one-time pre-implementation testing, reliance on static performance metrics, and periodic audits. Addressing these challenges requires governance frameworks that clarify accountability, uphold human judgment, and support ongoing evaluation of AI systems in clinical practice. By framing prescription verification as a socio-technical activity rather than solely a technical function, this editorial advances the discourse on the responsible integration of AI into medication safety workflows.