Medication errors including incorrect drug selection, dosage, timing, or patient identificationremain among the most prevalent and preventable causes of patient harm across both hospitaland home-care settings. The World Health Organization has identified medication safety as aglobal priority, estimating that preventable medication-related harm results in economic costsexceeding 42 billion annually. Intelligent Medication Management Systems (IMMS), which integrate artificial intelligence (AI), Internet of Things (IoT) technologies, and automated dispensing mechanisms, haveemerged as a promising paradigm for reducing these errors and improving overall medicationsafety. These systems enable real-time monitoring, decision support, and closed-loop controlacross the medication lifecycle from prescription and dispensing to administration andadherence tracking. This paper presents a comprehensive review of IMMS, covering key implementationsincluding automated dispensing cabinets in clinical environments, smart medicationadherence systems for home care, AI-driven drug interaction and dosage optimizationmodels, and fully integrated closed-loop medication delivery systems. It examines theunderlying technological components, including sensor networks, interoperability standards, machine learning models, and system architectures that enable reliable and scalabledeployment. In addition, the paper critically analyzes current limitations and challenges, includingfragmentation across healthcare data systems, regulatory complexities surroundingsoftware-as-a-medical-device, cybersecurity vulnerabilities in connected medicalinfrastructure, and the emergence of new systemic risks associated with automationdependency. Finally, the review outlines future directions driven by advances in wearable sensing, edgeAI, and personalized medicine, highlighting the potential for IMMS to transition fromreactive safety systems to proactive and predictive healthcare infrastructures.
Aadhar Gupta (2026) studied this question.