## 3 Discussion
Many studies highlight the need for larger, multi-institutional dataset and the necessity to conduct multicenter trials is critical to reduce biases and test the stability and reliability of AI models. In addition, more advanced algorithmic developments are necessary to improve the recognition of complex cases, including severe periodontal disease and anatomically challenging structures. Current CNN-based models often struggle with such cases due to their limited ability to capture subtle variations in anatomical structures and disease progression. Refining these models by incorporating hybrid deep learning techniques, such as transformer-based architectures and multi-scale feature extraction methods, could enhance diagnostic performance by allowing the models to capture finer details and hierarchical features more effectively. Leveraging synthetic data generation and domain adaptation techniques may help compensate for the scarcity of severe cases in training datasets, improving model robustness. Methods like active learning, where models iteratively refine their learning process based on the most uncertain cases, could further enhance AI accuracy in challenging scenarios. Moreover, integrating multimodal data, including clinical records, intraoral scans, and patient history, could provide a more holistic understanding of disease progression, improving the model's diagnostic precision [53]. Second, the development of interpretable and user-friendly AI tools is vital for ensuring seamless integration into clinical practice. Models that offer clear explanations for their predictions not only foster trust among practitioners but also facilitate collaboration between AI and human expertise. In fact, model interpretability remains a key concern, as black-box AI systems pose challenges in clinical decision-making by limiting clinicians' ability to understand and validate AI-driven diagnoses. A lack of transparency can reduce trust in AI-assisted tools, potentially hindering their adoption in real-world settings. To address this, techniques such as Grad-CAM visualizations, feature attribution methods, and hybrid approaches that integrate deep learning with traditional machine learning can enhance interpretability. These methods provide insight into model decision-making processes, allowing for more transparent and explainable AI applications in periodontal diagnostics. Additionally, the development of rule-based systems and attention mechanisms further aids in bridging the gap between AI predictions and clinical reasoning, fostering greater confidence among practitioners. Real-world deployment requires rigorous validation and regulatory approval, necessitating collaborations between AI researchers, dental clinicians, and regulatory bodies to ensure clinical efficacy and safety [54; 55; 56]. Retrospective study designs, while valuable for analyzing existing data, often fall short in capturing the dynamic variabilities and issues encountered in real-time clinical practice. These studies depend on historical data, which may not reflect the current clinical environment or the rapid advancements in medical technology and treatment protocols. As a result, the findings from retrospective analyses might not fully account for the complexities and nuances of actual clinical scenarios, where patient conditions and treatment responses can vary significantly. This limitation can lead to a gap between the study outcomes and their applicability in real-world settings, potentially affecting the generalizability and relevance of the research conclusions. To address these challenges, it is crucial to complement retrospective studies with prospective research and real-time data collection, ensuring that AI models and clinical insights are robust and adaptable to the ever-evolving landscape of healthcare. To effectively address the challenges of interoperability and data quality in the application of AI in periodontology, several strategies can be implemented.