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M. Bidar , P. Ghaziani, M. Saatchi , Ma. Soluti ,
Volume 14, Issue 3 (9 2001)
Abstract

Endodontic postoperative pain is still one of the major problems for dentists. According to the researches periapical inflammation after RCT is one of the most important factors causing endodontic postoperative pain. Histamine is one of the effective chemical mediators, which produces such inflammation. So, for controlling pains after RCT, the factors reducing inflammation should be found. The aim of this study was to investigate pain control after RCT by drug prophylaxis with antihistamine (asetemizole). 60 patients were divided in 2 groups (30 patients in each group). Group 1 had a capsule of  asetemizole (20mg) and the second group had a placebo capsule one hour before RCT. The patients completed the questionnaire after RCT and gave it back on the next session. The evaluated times were 1, 3, 6, 9, 12, 18, 24 and 72 h after RCT. This study indicated that asetemizole was able to reduce the moderate pericemental pain just at the 9 and 12 hours after RCT, and it was not able to reduce the spontaneous pain after RCT significantly on the evaluated times


Bita Kheiri, Mona Fazel Ghaziani,
Volume 39, Issue 0 (3-2026)
Abstract

Background and Aims: In recent years, the use of artificial intelligence (AI) has become increasingly common in dentistry because it facilitates the process of diagnosis and clinical decision-making. It is necessary for dentists to be aware of the advantages and disadvantages of artificial intelligence before implementing it. The present study aimed to comprehensively review the various applications of artificial intelligence in the diagnosis of dental diseases along with its challenges and disadvantages.
Materials and Methods: For this review article, a complete search was conducted on the PubMed and Google Scholar databases and studies published in recent years as well as studies published in 2024 were collected using the keywords "artificial intelligence," "dentistry," "diagnosis." Finally, the relevant articles were selected and evaluated, focusing on artificial intelligence in dentistry and the diagnosis of dental diseases.
Results: Advances in artificial intelligence in dental imaging, particularly through machine learning (ML) and artificial neural networks (ANN), have dramatically transformed the way dental disease is diagnosed. These technologies help dentists to analyze complex information and produce more accurate results by using algorithms that allow systems to learn and respond to data. The most recent development in this area is deep learning (DL), which uses multiple layers of neural networks to process unlabeled data and predict outcomes. These techniques are used in various fields such as diagnostic imaging, periodontology, dental caries detection, and osteoporosis screening, which help to improve the quality of dental services. Despite the benefits of AI in clinical dentistry, three controversial challenges remain and need to be addressed: ease of use, return on investment, and evidence of performance, or reliability.
Conclusion: Based on the results, the most important advantage of AI is the diagnosis of dental diseases. AI has great potential to reduce the pressure on health systems by automating routine tasks and improving patient care. However, this technology can never replace human expertise and must be guided by ethical principles. Ultimately, AI is recognized as a valuable tool in dentistry and the final decision-making always remains with the dentist.


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