Large Language Models and Machine Learning Framework for Predicting Dental Ceramics Performance.

ABSTRACT:

Introduction and aims

Clinical fractures remain the primary cause of failure in dental all-ceramic restorations, highlighting the need to improve the mechanical performance and durability of ceramic material. This study aimed to develop a large language model (LLM)-based framework to automatically construct a structured database of dental ceramics and integrate it with machine learning (ML) to predict material properties and accelerate material design.

Methods

LLMs (Llama, Qwen, and DeepSeek) were employed to perform literature mining tasks, including text classification, information extraction from abstracts, and tabular data extraction. These processes were integrated into an automated pipeline to systematically extract and structure compositional and performance data from

SUBMITTER: Zhou H 

PROVIDER: S-EPMC12805014 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature