## 7 Conclusions and Future Prospective  
CH preservation may greatly benefit from the application of EM techniques, which are among the most Non-Invasive (NI) and Non-Destructive (ND) available methods, and may also respond better than others to the variety of tangible CH in terms of materials, shapes, and manufacturing procedures. The most investigated sector is that of painting, which also attracts the most economic attention from different stakeholders. Pigment investigation, artist's techniques, previous interventions, and EM techniques may provide insights into these aspects. Pigment mapping is also a crucial aspect of Modern and Contemporary Art preservation, which remains a significant challenge awaiting comprehensive answers--thus, a field that is completely open for future development. THz spectroscopy and imaging can differentiate pigments through transmission and reflection techniques, revealing both original and restoration materials. However, more research is needed, particularly in developing THz spectral library databases. Conversely, AI, including Machine Learning (ML) and Deep Learning (DL), has shown significant progress in CH applications, ranging from damage detection to 3D reconstruction. ML identifies patterns in large datasets, while DL aids in reconstructing damaged artifacts. AI also enhances the integration of CH into interactive virtual and augmented reality experiences. Despite these advancements, human expertise remains essential for interpreting complex tasks and validating AI results. The collaboration between human knowledge and AI will be vital for addressing the challenges in cultural heritage preservation. The main weakness is the absence of standardized best practices. This issue is highlighted with varying degrees of emphasis throughout much of the reviewed literature. For example, the application of hyperspectral imaging (HSI) techniques for colorimetric analysis of paintings before and after restoration is still rare due to difficulties in obtaining accurate, reliable, and reproducible data suitable for matching colorimetric calculations as required by CIE [111] (p. 3). The lack of standards is also emphasized by [PERSON] et al., who focus on multispectral imaging (MSI) application to heritage artifacts. They note that \"there is no current standard regarding the application of MSI to heritage artifacts using systems with narrowband light sources, and the reporting of this activity in the published literature lacks cohesion\" [109] (Introduction). The absence of standards is similarly highlighted in the application of MW heating by [PERSON], who points out that there is no \"systematic approach to the planning of artwork treatments with microwave heating\" [213]. The lack of standards indicates space for future development. Another important area for future progress is the design and implementation of increasingly lighter instrumentation that allows for in situ investigation and can be applied to different artifact typologies. We reported an example in Figures 10 and 11. Modular instrumentation that can be adapted to various needs--even within the same museum--is a promising future direction. An additional significant gap, not previously mentioned, is the scarcity of large datasets necessary for the effective training of neural networks [113]. The limited availability of high-quality datasets continues to pose a major challenge to the advancement of neural network applications in this field. To overcome this obstacle, industry experts should consider implementing several targeted strategies, as detailed in the following Table 26.  
Finally, future directions involve developing modular, lightweight instruments for in situ analysis, expanding spectral libraries for THz spectroscopy, and creating large, annotated datasets to improve AI methods such as machine learning (ML) and deep learning (DL). Collaborations with human experts are essential for validating AI results. Addressing these issues through standardization, data expansion, and innovative tools will advance CH preservation.