<![CDATA[The development of Artificial Intelligence (AI) technology has transformed operational paradigms across various sectors of human life in the 21st century. Among the diverse branches of AI, Computer Vision (CV) and Expert Systems stand as two of the most widely implemented technological pillars, yet they possess starkly contrasting operational characteristics. This study aims to conduct an in-depth comparative analysis regarding the effectiveness, architecture, opportunities, and implementation challenges of both Computer Vision and Expert System technologies in the digital era. The method employed in this research is a comparative Systematic Literature Review (SLR), analyzing secondary data from 16 reliable sources including national journals, international journals, and recent academic modules. The data analysis process focuses on three main dimensions: system architectural methodology, functional efficiency in problem-solving, and the multidimensional impacts on the education, business, security, and user health sectors. The results indicate that Computer Vision excels in processing unstructured data (such as real-time images and videos) based on neural models (Convolutional Neural Network), but demands high computational power and poses a user health risk in the form of Computer Vision Syndrome. On the other hand, Expert Systems excel in declarative knowledge representation based on logical rules (rule-based) for deterministic, structured, and infrastructure-efficient decision-making, though they remain rigid against dynamic data changes. In conclusion, these two technologies are not mutually exclusive but rather complementary; integrating both into a Hybrid AI system represents the future direction for creating autonomous systems that are not only capable of visual perception but also capable of cognitive reasoning.]]>
