AI-Driven Multimodal Clinical Intelligence in Dentistry: Integrating Natural Language Processing, Machine Learning, and CBCT Imaging for Endodontic Treatment Planning
Keywords:
Artificial intelligence, multimodal systems, dentistry, endodontics, CBCT imaging, large language models, clinical decision support, machine learningAbstract
Modern healthcare is being transformed by artificial intelligence, as it allows clinical decision-making to become more accurate, efficient, and data-driven. The field of dentistry, specifically endodontics, is an important area where intelligent systems can be applied due to its reliance on imaging and clinical interpretation.
This paper proposes a multimodal clinical intelligence model that involves clinical text analysis using large language models, machine learning predictive systems, and cone-beam computed tomography imaging to improve treatment planning. The framework uses heterogeneous data sources to provide a unified diagnostic perspective, enhancing workflow accuracy and efficiency.
The experimental results demonstrate significant improvements in diagnostic accuracy and reliability of treatment recommendations compared to traditional approaches. The study highlights the importance of combining structured and unstructured clinical data and contributes to the development of next-generation intelligent dental care systems.
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