Artificial intelligence has made significant strides in dentistry, especially in areas like diagnostics and clinical imaging. However, the application of AI in the design and translation of novel dental biomaterials is still in its infancy. Recent research explores AI and machine learning's applications in predicting the behavior and properties of these materials, ranging from mechanical to biological attributes. Traditional and modern AI models, such as deep learning and physics-informed networks, are now being utilized to anticipate molecular-to-macroscale interactions in dental biomaterials before undergoing clinical assessments. Yet, the full power of AI in enabling inverse design, integrating multi-scale data, and ensuring the responsible and efficient transition of smart materials to the clinic presents a significant and exciting frontier. This Research Topic aims to consolidate efforts and accelerate progress at this critical intersection.

The primary goal of this Research Topic is to leverage artificial intelligence and machine learning technologies to revolutionize the field of dental biomaterials. By addressing specific questions on property prediction, inverse design, and integrating multi-scale and diverse datasets, this research topic seeks to advance the role of AI in designing next-generation dental products. Furthermore, it aims to promote transparency, reproducibility, and adherence to regulatory standards through research on explainable and responsible AI models, facilitating a seamless transition of innovations from the laboratory to clinical environments.

To gather further insights into the AI-driven advancement in dental biomaterials, we welcome articles addressing, but not limited to, the following themes:

1. AI-driven discovery, property prediction, optimization, and design of dental biomaterials
2. Generative models for the design of innovative dental ceramics, composites, and adhesives
3. Integrating lab, simulation, and real-world data for material optimization
4. Transparent and responsible AI methodologies for dental science

Original research, protocols, in silico studies, critical reviews, and technical perspectives are all welcome. We particularly encourage work that pushes toward open science, reproducibility, and interdisciplinary collaboration between data scientists, materials researchers, clinicians, and regulatory experts.

Are you interested in contributing a manuscript to this Research Topic?

AI-Powered Innovations in Dental Materials: From Bench to Chairside