Natural Language Processing and Machine Learning Integration for Clinical Decision Support in Endodontics Using Biomedical Text Analytics and 3D Imaging Data
Keywords:
Artificial intelligence, clinical decision support systems, endodontics, CBCT imaging, natural language processing, machine learning, healthcare analytics, diagnostic systemsAbstract
Artificial intelligence continues to transform healthcare by improving diagnostic processes and supporting clinical decision-making. In endodontics, the complexity of root canal systems and the limitations of conventional diagnostic techniques necessitate the use of advanced technologies. This paper presents an artificial intelligence-based clinical decision support system integrating natural language processing, machine learning, and cone-beam computed tomography (CBCT) imaging. The proposed framework utilizes multimodal data to enhance diagnostic accuracy and treatment planning. Simulated and clinical datasets were used to develop and evaluate a hybrid deep learning model. The results demonstrate improved precision in diagnosing periapical lesions, enhanced interpretation of root canal morphology, and improved prediction of treatment outcomes. The system also reduces diagnostic variability and supports clinicians with data-driven insights.
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