Explainable AI in Clinical Pharmacy Decision Support Systems
Keywords:
Explainable Artificial Intelligence, Clinical Pharmacy, Clinical Decision Support System, Medication Safety, Drug Interaction Prediction, Personalized Medicine, Machine Learning, Healthcare Informatics, Author Name, Scopus, Springer, Journal Name, Wissira, Journal Short Form, Wissira Press, Wissira Research Lab, Research Gate, SSRN, ISSN, Academia, UGC Care, PubMed, WOSAbstract
Clinical Pharmacy Decision Support Systems (CPDSS) are increasingly integrating Artificial Intelligence (AI) to improve medication safety, optimize therapeutic outcomes, and support evidence-based clinical decision-making. However, many advanced AI models operate as "black boxes," limiting clinician trust and regulatory acceptance. Explainable Artificial Intelligence (XAI) addresses this limitation by providing transparent, interpretable, and clinically meaningful explanations for AI-generated recommendations. This manuscript explores the role of XAI in enhancing medication management, adverse drug event prediction, drug-drug interaction detection, and personalized pharmacotherapy. It reviews recent developments in explainability techniques and their application in pharmacy practice while highlighting current challenges and future opportunities. The discussion emphasizes that explainable AI has the potential to improve healthcare quality, patient safety, and clinician confidence in AIassisted pharmacy services
