Responsible implementation of artificial intelligence across the medication-use process: an evidence-informed framework for medication safety.

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.