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Related Concept Videos

Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Modified-Release Drug Delivery Systems: Overview01:19

Modified-Release Drug Delivery Systems: Overview

Modified-release dosage forms are designed to address the limitations of drugs with short biological half-lives. These forms maintain stable therapeutic drug concentrations over extended periods, reducing the need for frequent dosing. A consistent drug level helps minimize peak-trough fluctuations, which can reduce adverse effects, lower the risk of drug resistance, and improve overall treatment effectiveness.One common type of modified-release form is the extended-release (ER) formulation. ER...
Modified-Release Drug Delivery Systems: Rate-Programmed II01:19

Modified-Release Drug Delivery Systems: Rate-Programmed II

Rate-programmed drug delivery systems release drugs in a controlled manner to maintain therapeutic levels. Three main designs include reservoir, matrix, and hybrid systems.Reservoir systems consist of a drug core enclosed within a membrane that controls drug release. In non-swelling reservoir systems, polymers like ethyl cellulose or polymethacrylates are used. These do not hydrate in aqueous media and control release through membrane thickness, porosity, or insolubility. This type includes...
Modified-Release Drug Delivery Systems: Classification01:23

Modified-Release Drug Delivery Systems: Classification

Modified-release drug delivery systems improve drug efficacy and minimize side effects by controlling the rate and location of drug release. These systems fall into three categories: rate-programmed, stimuli-activated, and site-targeted.Rate-programmed systems release drugs at a predetermined rate, maintaining consistent therapeutic levels and reducing fluctuations that could lead to toxicity or subtherapeutic effects. These systems use polymeric matrices, reservoir-based designs, or osmotic...
Modified-Release Drug Delivery Systems: Rate-Programmed I01:22

Modified-Release Drug Delivery Systems: Rate-Programmed I

Rate-programmed drug delivery systems (DDS) are designed to release drugs at specific, controlled rates to maintain consistent therapeutic levels. These systems are categorized based on their release mechanisms, including dissolution-controlled DDS, diffusion-controlled DDS, and combined dissolution-diffusion-controlled DDS.In dissolution-controlled DDS, the release rate depends on the slow dissolution of the drug itself or the surrounding matrix. Drugs with inherently slow dissolution rates,...
Biopharmaceutical Factors Influencing Drug Product Design: Overview01:22

Biopharmaceutical Factors Influencing Drug Product Design: Overview

Rational drug product design integrates knowledge of the drug’s physicochemical properties, formulation components, manufacturing techniques, and intended route of administration. Each factor influences the drug’s performance, including how it is released, absorbed, and eliminated in the body.The physicochemical properties of a drug—such as solubility, stability, and particle size—affect its compatibility with excipients and the choice of dosage form. Excipients, though pharmacologically...

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Related Experiment Video

Updated: Jun 27, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Design and Validation of a Cyber-Physical Medication Dispensing Platform Integrating Edge AI Verification,

Buddharaksa Phatcharasaksakol1, Supaphan Sittithanon1, Veerinrada Pianapitham1

  • 1International School of Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

This study introduces a novel cyber-physical medication dispensing platform using edge AI for accurate pill verification. The system achieved an 86.25% success rate, enhancing patient safety in medication management.

Keywords:
Internet of Things (IoT)automated medication dispensingcyber–physical systems (CPSs)edge computingsmart healthcare systems

Related Experiment Videos

Last Updated: Jun 27, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Area of Science:

  • Robotics and Automation
  • Artificial Intelligence in Healthcare
  • Cyber-Physical Systems

Background:

  • Medication dispensing errors pose significant risks, especially for elderly patients requiring long-term management.
  • Ensuring accurate medication delivery is crucial for patient safety and effective treatment outcomes.
  • Current systems may lack the precision and real-time verification needed to prevent errors.

Purpose of the Study:

  • To design and validate a cyber-physical medication dispensing platform.
  • To integrate edge AI for real-time visual verification of dispensed medications.
  • To improve the accuracy and safety of automated medication dispensing systems.

Main Methods:

  • Development of a platform integrating robotic manipulation, edge AI (YOLOv8 on Hailo AI accelerator), distributed motion control, and cloud synchronization.
  • Utilized a rotary medication storage, vacuum-based pill handling, and a Klipper control framework.
  • Experimental validation using an environment-specific dataset for perception subsystem evaluation and end-to-end dispensing trials.

Main Results:

  • The perception subsystem achieved a precision of 0.627, recall of 0.739, and mAP@0.5 of 0.786.
  • An adaptive verification strategy improved dispensing accuracy under varied conditions.
  • End-to-end testing demonstrated an 86.25% overall dispensing success rate with zero incorrect dispensing events.

Conclusions:

  • The integrated cyber-physical platform demonstrates the feasibility of edge AI verification for medication dispensing.
  • This system offers a robust foundation for perception-assisted dispensing in smart healthcare.
  • Further research can focus on long-term deployment and clinical validation for enhanced patient safety.