%0 Journal Article
%T Artificial Intelligence as a Second Reader for High-Risk Prescriptions rather than a First Decision Maker
%A Sara Al-Fahd
%A Noura Al-Khalifa
%A Omar Al-Turki
%J Archives of Pharmacy Practice
%@ 2320-5210
%D 2025
%V 16
%N 2
%R 10.51847/ROT7TCFRhD
%P 35-43
%X Artificial intelligence is increasingly positioned near medication decisions, yet the safest location for algorithmic assistance remains unresolved. Treating AI as the first reader may frame the case before a pharmacist has independently interpreted the prescription, while treating it as a decision maker may blur professional authority, encourage automation bias, and conceal uncertainty. This article proposes an Independent Human–AI Prescription Second-Reader Model for selected high-risk prescriptions. The model begins with a human first read, followed by a separately configured AI review activated through a locally validated risk-trigger gate. The AI may detect, prioritize, challenge, explain, or abstain, but it may not prescribe, authorize, independently release a prescription, or adjudicate unresolved conflict. Human and AI findings are reconciled through six proposed states: concordant clearance, concordant concern, AI-only concern, human-only concern, competing interpretations, and unresolved uncertainty or abstention. Disagreement initiates data verification, contextual recovery, independent professional reassessment, prescriber clarification, and proportionate escalation. The model also incorporates auditability, subgroup assessment, workload monitoring, drift surveillance, version control, and suspension or requalification after material change. Evaluation must address incremental interception of clinically important prescription problems together with false reassurance, misleading challenge, delay, alert burden, inequity, and responsibility diffusion. The contribution is conceptual rather than validated: it organizes independent review, authority boundaries, conflict resolution, and governance into a testable human–AI safety architecture. Its applicability remains conditional on prescription type, data quality, professional capacity, local workflow, external validation, and prospective evaluation of the combined work system. 
%U https://archivepp.com/article/artificial-intelligence-as-a-second-reader-for-high-risk-prescriptions-rather-than-a-first-decision-5f2stfljw1dv12d