Machine Learning-Powered AI Chatbots in Pharmacy Practices: A Survey of Techniques, Applications, and Future Directions

Abstract

Artificial intelligence (AI) and machine learning (ML) technologies have increasingly transformed pharmacy practice by enhancing medication management, pharmaceutical counseling, and adverse drug reaction (ADR) monitoring. Among the most impactful innovations are MLdriven chatbots, which integrate natural language processing (NLP), deep learning, and reinforcement learning to simulate pharmacist-patient interactions. These chatbots offer round-the-clock, personalized drug guidance, improve medication adherence, and reduce pharmacist workload, particularly in underserved areas. This survey presents a comprehensive review of machine learning methodologies employed in pharmacy chatbots, including supervised, unsupervised, and hybrid models. Real-world applications are analyzed, focusing on medication adherence monitoring, prescription validation, patient education, and pharmacovigilance. The paper also explores current limitations, such as data privacy risks, linguistic challenges, and fragmented regulatory standards. Ethical concerns, including algorithmic bias and lack of transparency, are discussed. Finally, future research directions are proposed, emphasizing the need for explainable AI, federated learning for data protection, and integration with wearable devices. By addressing these gaps, ML-generated chatbots can be positioned as reliable, scalable, and patientcentered tools within digital healthcare ecosystems, supporting safer and more efficient pharmacy services.

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