AbstractDentistry is being rapidly transformed by artificial intelligence (AI), which has progressed from experimental prototypes to reliable therapeutic tools. When it comes to using radiographic analysis to identify caries, periapical pathology, periodontal bone loss, and oral lesions, deep learning models are currently on par with or better than human specialists. Prosthodontic design workflows are accelerated by generative models, and orthodontic landmarking has become more accurate. By simplifying healthcare decision-making, documentation, and communication, large language models (LLMs) improve patient engagement. Personalized risk stratification is made possible by predictive analytics, which enhances treatment planning and preventive care.There are still issues despite these developments. Biases in training data can jeopardize diagnostic equality, and AI systems frequently have trouble generalizing across diverse populations. Continued issues include data governance, regulatory compliance, and smooth integration into current dentistry workflows. The industry is adopting MLOps (machine learning operations) for scalable deployment, post-market surveillance to track actual performance, and human-in-the-loop validation to provide clinical oversight in order to address issues. In 2024 and 2025, chairside decision support solutions that provide real-time insights during patient visits will become more important. In order to ensure safe data transmission across platforms, emerging AI ecosystems strive to be both interoperable and privacy-preserving. With the ability to interpret text, pictures, and 3D scans, multimodal foundation models hold the potential to unite therapeutic and diagnostic workflows and usher in a new era of intelligent, patient-centered dental care.
Kumar et al. (Wed,) studied this question.