Research in social & administrative pharmacy : RSAP · 2026
Responsible implementation of artificial intelligence across the medication-use process: an evidence-informed framework for medication safety.
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Gobran Yahia
Аннотация
BACKGROUND: Artificial intelligence (AI) is increasingly used across medication-use workflows, but discrimination alone does not establish safe or useful implementation.
OBJECTIVE: To propose an evidence-informed framework for responsible implementation of AI across the medication-use process.
METHODS: A targeted critical evidence scan through 28 July 2026, updated on 17 September 2026, charted 17 clinical studies. Domains were derived through inductive coding, comparison with clinical-AI and pharmacy guidance, and structured study-level appraisal.
RESULTS: The Medication-Use AI Safety and Implementation Framework contains seven linked domains: clinically meaningful target and label; transportable validation; calibration and threshold justification; clinically actionable information and explanation; workflow integration and human oversight; prospective clinical evaluation; and lifecycle governance and resilience. Human factors and equity are cross-cutting. Medication-specific criteria emphasize severity-weighted false negatives, alert burden, pharmacy capacity, formulary and workflow dependence, safety nets for low-risk suppression, and contingency planning.
CONCLUSIONS: The framework is a proposed implementation structure, not a validated minimum standard or readiness score. It is intended to help medication-use professionals judge whether AI systems have sufficient clinical, operational, and governance evidence to progress responsibly toward prospective use. Stakeholder consensus, reliability testing, and prospective validation are required.
OBJECTIVE: To propose an evidence-informed framework for responsible implementation of AI across the medication-use process.
METHODS: A targeted critical evidence scan through 28 July 2026, updated on 17 September 2026, charted 17 clinical studies. Domains were derived through inductive coding, comparison with clinical-AI and pharmacy guidance, and structured study-level appraisal.
RESULTS: The Medication-Use AI Safety and Implementation Framework contains seven linked domains: clinically meaningful target and label; transportable validation; calibration and threshold justification; clinically actionable information and explanation; workflow integration and human oversight; prospective clinical evaluation; and lifecycle governance and resilience. Human factors and equity are cross-cutting. Medication-specific criteria emphasize severity-weighted false negatives, alert burden, pharmacy capacity, formulary and workflow dependence, safety nets for low-risk suppression, and contingency planning.
CONCLUSIONS: The framework is a proposed implementation structure, not a validated minimum standard or readiness score. It is intended to help medication-use professionals judge whether AI systems have sufficient clinical, operational, and governance evidence to progress responsibly toward prospective use. Stakeholder consensus, reliability testing, and prospective validation are required.