Artificial Intelligence and Robotics for Automated Pharmacy and Medicine Dispensing Systems: A Survey

Abstract: Manual medicine dispensing, drug storage, and pharmacy inventory management remain error-prone, slow, and labor-intensive, motivating a rapid rise in artificial intelligence (AI) and robotics research aimed at automating these processes. This paper presents a structured survey of twenty recent works (2020–2026) spanning robotic drug grasping, vision-based pill/drug identification, automated dispensers and infusion devices, IoT-enabled inventory and supply-chain tracking, and broader reviews of AI/deep-learning techniques underpinning these systems. We propose a four-category taxonomy – Robotic Grasping and Manipulation, Vision-Based Detection and Identification, Automated Dispensing and Delivery Hardware, and IoT/Inventory and Supply-Chain Management – and examine each reviewed paper in terms of technique, dataset, reported results, and trade-offs. A comparative analysis table summarizes accuracy, dataset scale, and system complexity across studies. Our findings show that YOLO-family detectors and hybrid grasp-planning networks dominate vision-guided manipulation, achieving 90–99% detection accuracy, while systematic reviews confirm consistent, though heterogeneous, gains in dispensing efficiency and medication safety from automation. We identify six open challenges, including limited real-world/clinical validation, dataset scarcity, edge-deployment constraints, and lack of standardized benchmarks, and outline future research directions to guide subsequent work in intelligent pharmacy automation.

Keywords: pharmacy automation, robotic grasping, medicine dispensing, YOLO, deep learning, IoT, healthcare robotics