AI-Driven Outpatient Robotic Pharmacy: Enhancing Medication Management and Patient Outcomes

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@article{1713906,
      author = {Mostafa Mohamed Fahmy},
      title = {AI-Driven Outpatient Robotic Pharmacy: Enhancing Medication Management and Patient Outcomes},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {7},
      pages = {1982-1996},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1713906.pdf},
      abstract = {Outpatient pharmacies face increasing prescription volumes, more patients on multiple medications, and limited staffing, which can affect the safety and efficiency of prescription filling. Robotic systems can help by handling repetitive tasks such as picking, labeling, and storing medications. However, people are still needed for tasks such as prioritizing high-risk prescriptions, responding to alerts, managing exceptions, and counseling patients. Recent studies indicate that integrating Artificial Intelligence (AI) with robotics enables closed-loop prescription verification, enhances quality control, and supports system evaluation. This review examines peer-reviewed studies from 2020 to 2025 on the use of AI-enabled robotics in outpatient settings, covering pick-and-label robots, machine vision, tablet recognition, risk assessment, workflow optimization, demand prediction, and management. Following the PRISMA 2020 and PRISMA-S guidelines, 1,201 unique citations were screened, yielding 77 included publications. To assess evidence beyond prediction accuracy, the Operating Pharmacy Readiness (OPR) framework is introduced to evaluate workflow realism, system adaptability, and collaboration between pharmacists and robots. Findings show that robots increase prescription throughput and reduce delays, while AI is most effective as an assistant that tailors alerts to staffing levels and provides clear explanations to pharmacists. Studies from 2024 and 2025 highlight the importance of guideline-based monitoring, privacy protection, and adherence to clinical reporting procedures during initial implementation. The review recommends developing a system architecture, a checklist, and a research program to evaluate system adaptability and collaboration across sites.},
      keywords = {AI, Outpatient Pharmacy; Robotic Dispensing; Medication Safety; Machine Vision; Clinical Decision Support; Systematic Review; Saudi Vision 2030},
      month = {January},
      doi = {https://doi.org/10.64388/IREV9I7-1713906}
  }