On Large Language Models (LLM) and Multimodalities

Error-Tolerant Multimodal Vision-Language Models for Endodontic Triaging: A Cross-Sectional Study

To assess the performance of multimodal vision-language artificial intelligence models, optimised using quantisationaware training, in triaging endodontic treatment needs. The focus is on the ability to interpret endodontic radiographs while tolerating common image capture errors, including cone cutting, elongation, foreshortening, horizontal misalignment, over- and under-exposure and artefacts.

Comparative Evaluation of Vision–Language Models for
Detecting and Localizing Dental Lesions from Intraoral Images

To assess the efficiency of vision–language models in detecting and classifying carious and non-carious lesions from intraoral photo imaging.

Dental Loop Chatbot: A Prototype Large Language Model Framework for Dentistry

This paper outlines the development and preliminary evaluation of the chatbot as a scalable clinical decision-support tool designed for resource-limited settings