## 10 Future Perspectives  
In the future, metabolomics and proteomics can be developed by integrating their efficacy with other diagnostic technologies, such as AI and machine learning. These last ones have gained a lot of popularity in recent years in clinical applications. Artificial intelligence (AI) is the utilization of computers or computer-controlled systems to reproduce human intelligence. It consists of various specialized areas, including machine learning, expert systems, natural language processing, planning, robotics, affective computing, and computer vision [148]. In particular, expert systems, machine learning, and computer vision are important for selecting and validating biomarkers in liquid biopsy samples [149; 150; 151]. Using these techniques, AI algorithms can extract knowledge from data provided by the user or identify patterns in multidimensional data derived from salivary biomarker analysis and patient information from electronic health records, all without explicit request [152; 153; 154; 155]. AI models can subsequently predict new outcomes for independent patients or samples, based on the patterns learned from the biomarker dataset, like SalivaDB, Salivaomics Knowledge Base (SKB), which are open-source salivary biomarkers databases [149; 156; 157] (Figure 6). Integrating salivary metabolomics and proteomics with advanced diagnostic technologies, such as artificial intelligence (AI) and machine learning, is a promising method for enhancing periodontal diagnostics. AI-assisted saliva liquid biopsy platforms have already been evaluated for diagnosing periodontitis, showing the potential of these technologies in identifying specific biomarkers related to periodontitis and improving diagnostic accuracy. It still should be evaluated what the clinical applicability of this method and its costs are. Moreover, there is still the question of whether AI can substitute the clinician in its diagnostic capability. However, some scientists demonstrate that the clinician cannot be substituted by the AI, with the clinician still having the most accurate diagnostic power [158]. The validation of metabolomics and proteomics diagnosis of periodontitis will be very useful for clinicians. In fact, the incorporation of salivary biomarker analysis into clinical practice paves the way for personalized periodontal care. Salivary biomarkers have shown high diagnostic accuracy in distinguishing periodontal health from periodontitis, facilitating early detection, monitoring disease progression, and tailoring treatment strategies to individual patient needs [159; 160]. In particular, identifying specific metabolites and specific proteins related to periodontitis makes it possible to personalize the treatment plan and to define more precise prognostic categories [161]. The identification of periodontitis-related proteins and metabolites taken separately represents a promising source, but the combination of both would give a more complete panoramic view of periodontitis. In fact, the combination of metabolomics with proteomics will provide more information on the biological stratification of periodontitis and help construct the best discriminative diagnostic model within the framework of personalized and precision medicine [162; 163]. Metabolomic and proteomic profiles may help in building prognostic models, predicting clinical outcomes of periodontitis, such as probing depth or alveolar bone loss; in order to achieve this, there is the need for further studies in which clinical outcomes are associated with the progression of metabolomic and proteomic profiles in 1, 3, and 5 years [33; 164]. The limitation of this would be the standardization of the methods. To fully realize the  
Figure 6: Workflow: from the collection of saliva to data interpretation. The first step is to collect and prepare salivary samples from the patients. Then, untargeted analysis is driven by Nuclear Mass Resonance (NMS) or Mass Spectrometry (MS). The last step is to interpret the data.