Volume 7 • Issue 2 • PP: 13 –27 • 2026
Drug Classification in Clinical Pharmacy: Principles, Applications, and Implications for Patient-Centered Care
Open Access & Copyright
© 2026 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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Drug classification in clinical pharmacy is increasingly important for organizing medication knowledge, supporting therapeutic decisions, and improving patient-centered pharmaceutical care. As healthcare data become more complex, traditional rule-based classification approaches may be insufficient for interpreting heterogeneous drugrelated information, including medication histories, molecular descriptors, clinical indicators, adverse-event records, prescription patterns, and patient-specific risk factors. This review examines how artificial intelligence, machine learning, deep learning, feature selection, optimization, and decision-support systems contribute to drug classification in clinical pharmacy. The review discusses the role of intelligent models in medication safety assessment, drug–drug interaction monitoring, personalized therapy, pharmacy-service planning, and operational medication management. It also highlights major challenges related to data quality, interpretability, generalizability, workflow integration, and ethical implementation. Overall, intelligent drug classification can strengthen clinical pharmacy practice when computational models are transparent, clinically meaningful, and aligned with professional pharmaceutical judgment.
Keywords
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