Journal
CUREUS JOURNAL OF MEDICAL SCIENCE
Volume 14, Issue 7, Pages -Publisher
CUREUS INC
DOI: 10.7759/cureus.27405
Keywords
electronic brain; deep learning artificial intelligence; convolutional neural networks (cnn); ai; cnn; ann; artificial neural network; applications of ai; artificial intelligence in dentistry; artificial intelligence
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Artificial intelligence (AI) has gained significant presence and importance in various sectors, including dentistry. In endodontics, AI models such as convolutional neural networks and artificial neural networks have been applied for complex predictions and decision-making, showing potential in tasks such as studying root canal anatomy and predicting treatment outcomes. However, further certification of cost-effectiveness, dependability, and applicability is necessary before integrating AI models into routine clinical operations.
Artificial intelligence (AI) has remarkably increased its presence and significance in a wide range of sectors, including dentistry. It can mimic the intelligence of humans to undertake complex predictions and decision -making in the healthcare sector, particularly in endodontics. The models of AI, such as convolutional neural networks and/or artificial neural networks, have shown a variety of applications in endodontics, including studying the anatomy of the root canal system, forecasting the viability of stem cells of the dental pulp, measuring working lengths, pinpointing root fractures and periapical lesions and forecasting the success of retreatment procedures. Future applications of this technology were considered in relation to scheduling, patient care, drug-drug interactions, prognostic diagnosis, and robotic endodontic surgery. In endodontics, in terms of disease detection, evaluation, and prediction, AI has demonstrated accuracy and precision. AI can aid in the advancement of endodontic diagnosis and therapy, which can enhance endodontic treatment results. However, before incorporating AI models into routine clinical operations, it is still important to further certify the cost-effectiveness, dependability, and applicability of these models.
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