ENDO AI: A Novel Artificial Intelligence Framework for Predicting Treatment Outcomes in Endodontic Therapy

Introduction Artificial Intelligence (AI) is revolutionizing healthcare, and its impact on dentistry, particularly in endodontics, is becoming increasingly significant. AI technologies such as machine learning, deep learning, and computer vision are proving beneficial in the detection, diagnosis, and treatment of dental diseases. This review focuses on the application of AI in root canal therapy, examining how these technologies contribute to enhancing diagnostic accuracy and optimizing treatment planning. Methodology This research reviews existing literature on the use of AI in endodontics. It includes studies on AI's application in radiographic analysis, disease detection, and treatment outcome prediction. The review also explores the role of AI in dental education and training, providing insights into both the benefits and challenges of implementing AI technologies. Results AI has demonstrated remarkable potential in automating the detection of dental pathologies such as periapical lesions, aiding clinicians in making more accurate diagnoses. Machine learning and deep learning models have been effective in predicting treatment outcomes, which assists clinicians in planning root canal therapies more efficiently. AI tools are also enhancing educational frameworks in endodontics by providing simulations that help reduce human error and improve learning outcomes. Discussion Despite the promising results, there are challenges to the widespread implementation of AI in clinical practice. Issues such as data privacy, the transparency of AI algorithms, and the integration of these systems into existing clinical workflows remain significant obstacles. Additionally, the need for rigorous validation of AI tools in real-world settings and the training of clinicians to work alongside AI systems are critical considerations for the future. Conclusion AI is poised to revolutionize the field of endodontics, improving diagnostic accuracy, treatment planning, and educational practices. However, to fully integrate AI into routine clinical practice, addressing challenges related to data privacy, algorithm transparency, and workflow integration is essential. The continued development and refinement of AI technologies in this field hold great promise for enhancing patient care and clinical outcomes in endodontics.