Design and Implementation of Artificial Intelligence-Based Pharmacy Web Application

Abstract

The paper contributes to healthcare services, especially pharmacies, by transforming pharmacy operations into modern healthcare services that are more reliable and accessible for both professionals and patients in their daily operations. Pharmacies in many developing environments still depend on manual prescription handling, which increases the risk of errors and slows service delivery. Existing digital systems often lack intelligent features that assist with validation, drug checking, and record management. This gap limits their usefulness in busy pharmacy settings. The objective of this study is to design and implement an artificial-intelligence-based pharmacy web application capable of supporting accurate prescription processing and improving workflow efficiency. The system was developed using a client–server architecture. The backend was implemented with a relational database and server-side scripting, while the interface was built with standard web technologies. An artificial intelligence model was trained on structured prescription samples to identify inconsistent entries. The development process followed requirement analysis, system modelling, implementation, and functional testing. Performance evaluation focused on system responsiveness and the accuracy of the intelligent module. The artificial intelligence component achieved an accuracy level of 89 percent in detecting prescription inconsistencies. Functional tests showed successful execution of 97 percent of test cases. The average system response time during core operations was 0.8 seconds under simulated loads. User evaluation indicated improved consistency in drug entry and reduced processing errors. The findings suggest that incorporating artificial intelligence into pharmacy management systems can enhance medication safety and streamline routine tasks. The system provides a practical foundation for wider deployment and may support future extensions involving real clinical data and advanced decision support functions.

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