Title: An Expert System for Eye Disease Diagnosis Using the CLIPS Expert System Shell
Authors: Lena Zaharna, Alaa Abu Sultan, Samy S. Abu-Naser
Volume: 10
Issue: 7
Pages: 1-V-
Publication Date: 2026/07/28
Abstract:
: Artificial Intelligence (AI) has become an essential technology in healthcare, providing intelligent solutions for medical diagnosis, clinical decision support, and patient management. Among AI techniques, expert systems have demonstrated considerable effectiveness in emulating the reasoning processes of human specialists by utilizing structured knowledge bases and inference mechanisms. This paper presents the design and implementation of a rule-based expert system for the preliminary diagnosis of common eye diseases using the CLIPS (C Language Integrated Production System) expert system shell. The proposed system employs a knowledge base consisting of medical facts and production rules acquired from ophthalmology references and expert knowledge. User-reported symptoms are processed through a forward-chaining inference engine that matches facts with diagnostic rules to identify the most probable eye condition. The system addresses several common ophthalmic disorders, including eye discharge, bulging eyes (proptosis), diplopia (double vision), and ptosis (drooping eyelid), while also providing information about possible causes and recommended medical actions. The modular architecture of CLIPS facilitates efficient knowledge representation, rule management, and inference execution, making it an appropriate platform for developing medical expert systems. The proposed system offers an accessible and interactive tool for preliminary eye disease assessment, which may assist healthcare professionals in initial screening and support individuals in recognizing symptoms that require prompt medical attention. Although the system is not intended to replace ophthalmologists, it demonstrates the potential of rule-based artificial intelligence to improve the accessibility of medical knowledge and enhance early diagnostic support.