## 6 Conclusions and Future Trends
### Future Trends  
In addressing the future trends in dental imaging, it is essential to move beyond merely listing the existing literature and instead provide a forward-looking analysis that connects current advancements in the field with emerging technologies like large-scale models and digital twins. Dental imaging has evolved significantly with the integration of AI, particularly through ML and DL techniques, which have enhanced the precision, accuracy, and efficiency of diagnostics and treatment planning. However, the future promises even more transformative developments as the field moves toward more complex, large-scale models capable of handling vast datasets with improved computational power and generalization capabilities. One promising area is the application of large-scale models, particularly deep neural networks trained on massive datasets that include diverse dental images from multiple populations and sources. These models have the potential to standardize and optimize diagnostic procedures by reducing biases and increasing the robustness of AI-driven systems. As more data become available from different geographical regions and patient demographics, these large-scale models could contribute to more personalized and precise dental care, considering patient-specific variations that traditional models might overlook. Additionally, future research should prioritize standardized benchmarks that enable a direct and consistent comparison between AI model performance across various platforms. The performance metrics can vary due to differences in hardware and experimental configurations. This standardization could create a foundation for more robust applications in dental care. Another emerging concept with significant implications for the future of dental imaging is the digital twin. A digital twin is a virtual model that replicates a real-world system or object, allowing for real-time simulations, monitoring, and predictive analytics. In dentistry, digital twins could enable highly individualized care by creating a real-time, dynamic digital representation of a patient's oral cavity. Such a model would integrate not only dental images but also other relevant patient data, such as genetic factors, health records, and lifestyle information, to provide a holistic view of the patient's oral health. By continuously updating the digital twin as new data are obtained from regular imaging and clinical evaluations, practitioners could simulate potential outcomes of different treatment options, predict future dental issues, and optimize treatment plans with unprecedented precision. Digital twins would also support more effective patient monitoring, enabling dentits to make data-driven decisions and adjust treatments in real time based on how a patient's condition evolves. Future trends in AI-based dental image analysis are also expected to benefit from the integration of large language models, which could play an important role in enhancing diagnostic accuracy by integrating information from both structured image data and unstructured clinical data (e.g., patient histories and treatment plans). By doing so, large language models could offer contextual insights, assist in diagnostic decision making, and help generate personalized treatment recommendations based on a broader scope of patient data [71; 72]. Moreover, the integration of large-scale models and digital twins will likely lead to the emergence of predictive and preventive dentistry. Rather than focusing on reactive treatments after dental problems have arisen, these technologies can allow for the early detection of conditions that may not yet be visible or symptomatic. By analyzing trends in a patient's data over time, predictive models can forecast potential future issues and recommend interventions to prevent them.