## 6 The Role of AI and Its Subsets, ML, and DL in Artifact Analysis  
This section explores the advancements, challenges, and opportunities in AI, with a particular focus on its subsets: ML and DL. We will specifically examine their applications in real-time automated image analysis. AI can enhance material identification, deterioration detection, and artifact classification by integrating advanced imaging techniques. The integration of AI with Virtual Reality (VR) and Augmented Reality (AR) creates immersive experiences, allowing visitors to explore historical sites and interact with AI-generated characters. Additionally, AI facilitates the digitization of cultural artifacts, enhancing preservation efforts and providing researchers and the public with remote access to these invaluable resources. ML, a subset of AI, involves developing algorithms that allow computers to learn from data and make predictions or decisions without explicit programming. In this context, ML facilitates the automatic organization and cataloging of cultural heritage (CH) objects, monitoring their condition and enabling 3D reconstruction and preservation for improved documentation and analysis. Conversely, DL, a subset of ML and AI, focuses on algorithms inspired by the brain's structure, particularly neural networks. This branch of ML can analyze complex patterns in large datasets. DL techniques [186; 187], especially convolutional neural networks (CNNs), have revolutionized the field. CNNs excel in image analysis, enabling tasks such as defect detection and microstructural analysis through effective pattern recognition and image classification in computer vision, achieving up to 90% accuracy in these categories [187]. A notable trend today is the integration of AI and photogrammetry for reconstructing 3D heritage scenes [188]. This includes paintings, sculptures, manuscripts, and archaeological artifacts. Several studies referenced in [189] are recalled here. SCAN4 RECO, an \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**High Cost and Limited Availability of Equipment** & \begin{tabular}{c} **Technical** \\ **Complexity and Need for Trained** \\ **Personnel** \\ \end{tabular} & \begin{tabular}{c} **Lack of** \\ **Standardized** \\ **Protocols and** \\ **Spectral Libraries** \\ \end{tabular} & \
\begin{tabular}{c} **Limited Awareness** \\ **and Acceptance** \\ \end{tabular} &
\begin{tabular}{c} **Integration** \\ **Challenges** \\ \end{tabular} \\ \hline THz systems tend to be expensive and are not widely accessible, which limits their use in routine conservation work. & Operating THz instruments requires specialized knowledge and skills, creating a barrier for conservators who lack training in this technology. & The absence of universally accepted procedures and comprehensive structures & Many conservators and stakeholders are unfamiliar with THz capabilities or skeptical of its & Incorporating THz systems into existing workflows can be complex, requiring adjustments to current practices and information. & \\ \hline \end{tabular}
\end{table} 
Table 21: Exploring the barriers to adopting THz technologies for effective CH conservation. EU-funded project, combines 3D scanning, robotics, and AI to create digital reconstructions of damaged or destroyed cultural heritage (CH) objects [189]. Rekrei (formerly Project Mosul) is a crowdsourcing and AI project aimed at reconstructing CH sites that have been destroyed or damaged. Users can contribute photographs and other data, while AI algorithms assist in the digital reconstruction of lost heritage [189]. In particular, two prominent initiatives--reported in Table 22, \"SCAN4 RECO\" and \"Rekrei\"--demonstrate how these projects utilize advanced technological methods to digitally restore damaged or destroyed cultural artifacts and sites [189]. After a fire damaged parts of Notre Dame Cathedral in 2019, a digital twin model was developed for experimentation with reconstruction methods.