Comparative Study of AI-Driven Decision Intelligence and Traditional Image Processing–based Decision Systems

Traditional image processing systems that used rule-based algorithms and hand-defined features during along time have been used to extract and reason on visual information. Though they generate computationally efficientand transparent decision making with limited adaptability, these lack adaptability resulting in a performance constraintto the tasks in complex, dynamic environments. The more recent Artificial Intelligence (AI) and Decision Intelligence(DI) concepts proposed data-driven models that integrate machine learning, deep learning, and reasoning in context toimprove the accuracy of decisions. In this paper, Decision Intelligence powered by AI is compared to traditionaldecision-making systems based on image processing regarding methodology, performance, scalability, and generalapplicability in different fields like healthcare diagnosis, surveillance, and industrial control. The paper points out thefact that the two methods present complementary aspects and present a hybrid model that inherits the interpretativeabilities of conventional methods alongside the abilities to predict of AI-fueled DI to create a powerful real-worlddecision-making model.