# Multidisciplinary Applications of AI in Dentistry: Bibliometric Review
## 4 Discussion  
The analysis of our study indicated that, between 2000 and 2023, a diverse group of 647 authors from 63 countries and 478 institutions contributed to 244 publications on the application of AI in the dentistry field. These publications emphasize the extensive reach and diverse perspectives in this area of research. Notably, there was a marked escalation in the volume of publications between 2018 and 2023. We observed a significant rise in the number of publications, a trend that aligns with findings in broader literature [7]. This surge can be linked to a variety of crucial factors. [PERSON] et al. [40] conducted a comprehensive review, revealing a remarkable and unparalleled increase in research activity in AI dental publications, with an average annual growth of 21.6% over the last decade and a 34.9% increase per year over the last 5 years, particularly in digital diagnostic methods like radiology. [PERSON] et al. [41] further supported this trend, highlighting the improvement in AI techniques and their outcomes in dentistry, emphasizing AI's role in accurate patient management, dental diagnosis, prediction, and decision-making. Moreover, another research on dental esthetics indicates a growing interest in research trends and global productivity [42]. Notably, India and the United States have emerged as forefront leaders in this domain. Among these contributions, the University of Ankara, Eskisehir Osmangazi University, and Charlie Universitatsmedizin Berlin stand out for their significant impact and leadership in the field. Additionally, the work of highly cited authors like [PERSON] and [PERSON] has been influential in shaping the current and future directions of AI applications in dentistry [43]. These findings align closely with those reported in a similar study by [PERSON] et al. [7], reinforcing the global relevance and consistency in AI dentistry research trends. The transformative potential of AI in dentistry, as previously highlighted in our literature review, has been empirically validated by our bibliometric analysis. Between 2018 and 2023, there has been a pronounced surge in publications focusing on advanced AI techniques such as Deep Learning (DL) and Neural Networks (NNs). These methods  
Figure 9: Words co-occurrence correlation. have transitioned from experimental to core tools in dental AI applications, especially in the areas of diagnosis and treatment planning. Our findings indicate a significant emphasis on the use of CNNs (Convolutional Neural Networks) and ANNs (Artificial Neural Networks) for critical diagnostic tasks. CNNs, for example, have been increasingly applied to enhance the accuracy of diagnosing periodical lesions and identifying root anatomies from radiographic images, often producing results that surpass the diagnostic capabilities of experienced practitioners [44]. Similarly, ANNs have shown quite high accuracy in predicting post-operative pain and accurately placing cephalometric points on lateral cephalograms and CBCT [44,45]. Studies have highlighted that an ANN not only simplifies the process but also eases the workload for dentists [46]. This correlates strongly with the trends identified in our bibliometric analysis, where these technologies have been employed to analyze a variety of radiographic data including bitewings, periapical and panoramic radiographs, and CBCT scans. Radiographic data are essential for AI use in dentistry, as they serve as a critical resource for training AI models, enhancing accuracy in diagnostics, standardizing interpretations, and facilitating predictive analysis [47]. Notably, the literature underscores AI's capacity for improving image classification and segmentation. Segmentation allows AI systems to isolate specific areas within radiographs, such as distinguishing healthy tissue from pathological areas [48]. Once segmented, these images are classified, enabling detailed diagnostics.