Biomaterials and Artificial Intelligence: Predictive Modeling and Design

Abstract

The emergence of artificial intelligence (AI) with synergistic integration is currently a paradigm-shifting strategy for the direction of biomaterials development and design. This paper analyzes the connection between AI and biomaterials, explaining the significant influence of predictive modelling on the path of the area. By carefully investigating state-of-the-art studies and unique applications, it illustrates how AI-driven predictive modelling redefined biomaterial design and entered a new era of unusual accuracy and productivity. This research covers a wide variety of AI technologies, from deep neural networks to machine learning, that facilitates the development of prediction models that use large datasets to anticipate the behaviour, characteristics, and interactions of biomaterials. It examines how artificial intelligence (AI) may speed up the method of screening for viable materials, improve their qualities, and forecast there in vivo reactions. This can help translate beachside discoveries into clinical applications more quickly. This paper further explains the future prospects and problems in the field of biomaterials and AI integration, underlining the significance of interdisciplinary working together, standardization of data, and ethical concerns.

Keywords

• biomaterials
• artificial intelligence (ai)
• predictive modelling
• data analysis
• deep neural networks
• productivity