Artificial intelligence-centered decision support systems are designed to analyze complex data sets and provide insights to human decision-makers. These mechanisms are specifically developed to ensure faster, more accurate, and efficient decision-making processes in the era of big data. AI techniques—such as data mining, machine learning, and natural language processing—support decision-making across various domains. These systems are developed as hybrid approaches that allow both automated decisions and human intervention. The main goals are to enhance decision quality, reduce error rates, and increase organizational efficiency.

The core structure of AI-assisted decision support systems consists of data collection, processing, modeling, and interpretation of results. In this process, AI algorithms extract meaningful patterns from data and present them for use by decision-makers. Thus, they offer solutions to complex and multidimensional decision problems. The effectiveness of these systems is directly proportional to the accuracy of the algorithms used, the quality of the data, and the decision-maker's adaptation to the system. Furthermore, the transparency and explainability of the systems play a critical role in gaining user trust.

^{Industry Applications (Generated by Artificial Intelligence)}

Artificial intelligence-centered decision support systems are widely used in various sectors such as healthcare, finance, manufacturing, and transportation. In the field of healthcare, they provide significant advantages in disease diagnosis and treatment planning; in the finance sector, they are utilized for risk analysis and portfolio management; in manufacturing, for supply chain optimization; and in transportation, for route planning. However, in the application of these technologies, issues such as ethics, privacy, and data security must be carefully addressed. In order for decision support systems to operate reliably and fairly, algorithms must be free of bias and datasets must have strong representational capacity.

Core Components of Artificial Intelligence-Centered Decision Support Systems

Artificial intelligence-centered decision support systems generally consist of three main components: database management, model-based analysis, and user interface. Database management ensures the collection, storage, and preprocessing of large volumes of data to be used in the decision-making process. Model-based analysis includes modeling, analyzing the data, and generating decision recommendations using artificial intelligence techniques. The user interface presents the results of the system to the user in an understandable manner and enables interaction between the user and the system.

^{Decision Theory (Created by Artificial Intelligence)}

Database management is a crucial stage that directly affects the accuracy of decisions produced by the system. The quality, completeness, and currency of the data determine the model’s performance. Therefore, processes such as data cleaning, integration, and normalization are conducted meticulously. The methods used in model-based analysis range from traditional statistical approaches to advanced artificial intelligence techniques such as deep learning and reinforcement learning. Model selection is based on the problem type, data structure, and desired outcomes. The user interface is designed with visual and interactive features that allow decision-makers to quickly and effectively evaluate the system’s suggestions.

Technological Approaches and Application Areas

Data-Centered Approaches

The first phase of the data processing pipeline involves cleaning and transforming data from heterogeneous sources (e.g., sensors, text, images) into usable representations. In supervised learning, label quality is a key determinant of model generalizability. Reinforcement learning offers strong performance in online decision scenarios by optimizing the action-reward relationship through feedback loops. Large language models contribute to decision-making by translating rules and goals expressed in natural language into executable process flows.

Sectoral Applications

• Healthcare: Early warning systems in intensive care management; rapid triage solutions based on image classification in telemedicine.
• Finance: Risk-sensitive deep learning models for credit scoring and portfolio optimization; "glass-box" strategies to comply with regulatory explainability requirements.
• Public Administration: Algorithmic automation in high-impact decisions such as welfare eligibility and tax auditing; legitimacy concerns arise in the absence of oversight and transparency.
• Urban Planning: Multi-criteria decision support systems simulate transportation infrastructure and sustainability scenarios to optimize resource allocation in municipal planning.
• Industry 4.0: Real-time predictive maintenance and capacity planning functions in digital twin-based production lines.

Ethics and Security

Ethical and security issues are of critical importance in the development and implementation of AI-centered decision support systems. Transparency of decision systems is essential for users to understand the system and follow the rationale behind decision recommendations. Black-box models, whose decision-making processes are not interpretable, may lead to trust issues. For this reason, the concept of Explainable AI (XAI) has gained prominence in recent years.

Privacy and data security must be prioritized, especially in domains where personal and sensitive data are involved. Compliance with data protection laws and adherence to ethical standards are essential for the sustainability of these systems. Moreover, preventing algorithmic bias remains a key challenge to ensure fairness in decision-making processes.