INTEGRATION OF ARTIFICIAL INTELLIGENCE AND INTERACTIVE MODELING METHODS IN THE MANAGEMENT OF BIOTECHNOLOGICAL SYSTEMS

Abstract

The rapid development of biotechnological systems requires innovative approaches for efficient management, prediction, and optimization. The integration of Artificial Intelligence (AI) and interactive modeling methods represents a transformative framework for improving system stability, productivity, and adaptability. This paper explores theoretical foundations, practical applications, and future prospects of AI-driven interactive modeling in biotechnology. The study highlights how machine learning algorithms, neural networks, and simulation-based decision-support systems enhance monitoring, process control, and real-time optimization in complex biological environments. The results demonstrate that integrated AI-modeling systems significantly improve accuracy, reduce operational risks, and enable sustainable biotechnological innovation.