The Role of Artificial Intelligence in Reducing Medication Errors in Radiology Departments: A Quality Improvement and Patient Safety Study
Abstract
Medication errors in radiology—particularly those involving contrast media and radiopharmaceuticals—pose a significant risk to patient safety and healthcare quality. This study evaluates the role of Artificial Intelligence (AI) in minimizing such errors through integration into radiology workflows.
A mixed-method approach combining literature review and a proposed implementation model was used to assess AI tools such as Clinical Decision Support Systems (CDSS), machine learning algorithms, and automated verification systems.
The findings indicate that AI can reduce medication errors by improving dose accuracy, identifying contraindications, and enhancing patient verification processes. Additionally, AI supports compliance with international standards such as Joint Commission International (JCI).
This paper proposes a scalable implementation framework for AI adoption in radiology departments and highlights measurable improvements in safety outcomes.
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