• Image analysis and restoration: AI algorithms can analyze and restore old, damaged, or degraded (moving) images, sounds, paintings, and photographs. These algorithms can enhance image quality, remove noise, and even reconstruct missing parts of the artwork, aiding in preserving and restoring cultural artifacts. Examples listed in [27] are the prediction of the painting’s style, genre, and artist, the detection of fake artworks by stroke analysis, and the artistic style transfer using adversarial networks to regularize the generation of stylized images.” Further research deals with the automatic colorization of images [31] and the restoration of ancient mosaics [32].
• Object recognition and classification: AI-powered computer vision techniques enable automatic recognition and classification of cultural objects. By analyzing visual features and patterns, AI algorithms can identify and categorize artifacts, sculptures, and architectural elements [33], facilitating the organization and cataloging of museum collections. Examples are the prediction of color metadata, e.g., for textile objects [34], of technique, timespan, material, and place metadata for European silk fabrics [35], and the recognition and classification of symbols in ancient papyri [36].
• Translation and transcription: AI language models are capable of translating. e.g., ancient texts, inscriptions, and manuscripts into modern languages. They can also be used for modern languages by translating metadata or full-text content of heritage objects and related information, making sharing cultural heritage across languages easier. Other models can transcribe handwritten texts, allowing researchers and historians to access and understand historical documents and perform automated analysis (e.g., [37]).
• Automatic text analysis: This comprises various approaches [38]. An example is the automatic semantic indexing of pre-structured historical texts, which enables historians to mine large amounts of text and data to gain a deeper understanding of the sources (e.g., [39]); for example, tax lists or registers of letters sent to a historical entity [40].
• Virtual Reality (VR) and Augmented Reality (AR): AI technology supports the creation of immersive VR and AR experiences for CH sites and museums. Visitors can virtually explore ancient ruins, historical sites, or museum exhibitions, interacting with AI-generated virtual characters or objects to enhance their understanding and engagement with the cultural context [41,42].
• Recommender systems for personalized experiences: AI algorithms can analyze user preferences, historical data, and contextual information to provide personalized recommendations for CH experiences. Despite the risks of information filtering (e.g., [43]), use is to suggest relevant exhibits, customized tours, or tailored content, AI-powered recommender systems enhance visitor engagement and satisfaction, or—triggered by the advent of large language models (LLMs) such as GPT—dialogue and chatbot systems. Examples are the use of chatbots in museums [44,45] or recommender systems for CH collections (e.g., [46,47]).
• Cultural content analysis and interpretation: AI techniques, such as natural language processing (NLP), are used to analyze large volumes of cultural content, including literature, music, and artwork. This analysis can reveal patterns, themes, and cultural influences, providing valuable insights into historical contexts and artistic movements. Examples are metadata enrichment (e.g., [48,49,50]) and linking to open data sources (e.g., [33]).
• Heritage digitization and preservation: AI can be crucial in digitizing cultural artifacts and archives. By automating digitization processes and extracting knowledge, AI speeds up the preservation of CH, allowing researchers and the public to explore and study rare artifacts remotely. Several articles provide an overview of particular technologies, e.g., for 3D acquisition, such as laser scanning [51] or photogrammetry [52], and quantify their use [53]. AI-powered systems can monitor and analyze CH site environmental conditions, helping with early detection of potential threats such as humidity, temperature fluctuations, and structural damage. This real-time monitoring aids in the proactive conservation and protection of cultural landmarks (e.g., [54,55]).
• Multimodal analysis: AI is capable of bringing together different sources and types of data. Approaches include text, images [35], 3D models [56], audio [57], and video [58].
• AI supports or creates artistic expressions: Applying algorithms that analyze heritage objects (or entire collections) and extract information that either artists and other creators can use to create new works [59] or AI creating “artistic” expressions.

• Content-based image retrieval: Efficient retrieval and exploration of historical images based on visual similarity and content-based features. However, traditional ML technologies currently require large-scale training data [27,72,73,74], which are only capable of recognizing well-documented and visually distinctive landmark buildings [62] but fail to deal with less distinctive architecture, such as houses of similar style. Even using more advanced ML approaches or combining different algorithms [75] only allows the realization of prototypic scenarios [76,77].
• Image-based localization: Connecting images with the 3D world relevant for AR/VR applications requires estimating the original six-degree-of-freedom (6DOF) camera pose. While several methods exist for homogeneous image blocks [78,79], the problem becomes increasingly complex for varying radiometric and geometric conditions, especially relevant for historical photographs [80].
• Image recognition and classification: Identifying objects, scenes, or people depicted in historical images using deep learning models, such as CNNs. This field ranges from the detection of WW2 bomb craters in historical aerial images [81], via historical photo content analysis [82] to historical map segmentation [83,84,85].
• Semantic segmentation and object detection: Locating and recognizing specific objects or regions of interest within historical images using techniques like Faster R-CNN and YOLO. In semantic segmentation, to classify parts of images [74,86,87].
• Image restoration and enhancement: Repairing and enhancing degraded or damaged historical images through techniques like denoising, inpainting, and super-resolution [88,89].

• NLP techniques: Named entity recognition, part-of-speech tagging, sentiment analysis, and topic modeling. The most recent applications of CNNs and Transformer [93] are consistently successful in accurately extracting and reducing the number of errors even with unsupervised pre-training.
• Text classification algorithms: Naive Bayes, Support Vector Machines, and Random Forests.
• Sequence models: Hidden Markov models, conditional random fields, and recurrent neural networks.

• Preprocessing: Includes character recognition (e.g., OCR), unification, processing of spelling variations and alignment to controlled vocabularies (e.g., [94]).
• Postprocessing: Used to check and correct any OCR reading errors via neural network approaches [95].

• Object recognition and classification and semantic segmentation: In 3D/4D reconstruction of CH, ML-based technologies are currently used primarily for specific tasks. This involves AI models to identify specific architectural elements, artifacts, or decorative motifs, to recognize specific objects [72,73,74,96], and to preselect imagery [97,98]. Other tasks include AI-based semantic segmentation techniques to partition 3D models into meaningful regions or components [99].
• 3D model creation: Research has focused on developing AI-based algorithms for efficient and accurate 3D reconstruction of CH objects, buildings, and sites. Traditional algebraic approaches, as in photogrammetry, employ algorithms within equations, e.g., to detect, describe, and match geometric features in images [100] and to create 3D models. ML approaches are currently heavily researched and used for image and 3D point cloud analytics in CH (recent overview: [27]), but increasingly for 3D modeling tasks. Generative adversarial networks (GAN), a combination of the proposal and assessment components of ML, are frequently employed as approximative techniques in 3D modeling, e.g., for single photo digitization [101], completion of incomplete 3D digitized models [102,103] or photo-based reconstructions [104]. Recent approaches include neural radiance fields (NeRF) [105,106,107,108], which have shown strength in creating 3D geometries from sparse and heterogeneous imagery and short processing time [109,110].
• Image to visualization approaches: Approaches bypass the modeling stage to generate visualizations directly from imagery [72,111,112], e.g., by transforming or assembling image content (recent image generators like DALL-E [113], Stable Diffusion or Midjourney). Other approaches based on NeRF to predict shifting spatial perspectives even from single images [114] can predict 3D geometries.
• Use of ML algorithms to detect patterns, anomalies, or changes over time within 3D models (e.g., [54]). The analysis involves assessing the effectiveness of AI in extracting meaningful information from large-scale 3D datasets, supporting archaeological research, conservation efforts, or architectural analysis.

4.5. AI and Music

• Digitization and restoration: AI assists in digitizing and restoring deteriorating audiovisual materials, improving their quality and preserving their historical significance.
• Video summaries: Can speed up the process of finding content in audiovisual archives [142].
• Content analysis and knowledge extraction: AI algorithms analyze audio and visual elements within content to identify patterns, objects, scenes, speakers, and other relevant information. It can also help to spot biases and contentious terms and track semantic drift in metadata, supporting curators, cataloguers, and others in deciding on potentially updating catalog records [143].
• Metadata enhancement: AI enriches metadata for better content organization, search, and context by extracting keywords or using LLMs to organize and enrich metadata records at scale.
• Transcription and translation: AI-powered speech-to-text transcription and translation services make audiovisual content more accessible and understandable to a wider audience [144].
• Partial audio matching: Supports framing analysis in identifying segments in one source audio file that are identical to segments in another target audio file. Framing analysis can reveal patterns and biases in the way content is being recontextualized in the media to shape public discourse [145].
• Cross-modal analysis: AI techniques analyze both audio and visual components of content, facilitating holistic interpretation and understanding.
• Interactive storytelling and content-generation interfaces: AI-powered interactive narratives and documentaries engage users with historical events and cultural context. AI can further enhance access by using fine-grained and time-based data extracted by AI systems as a basis for creating “generous interfaces” that allow for the rich exploration of CH collections [146,147] and using conversational speech to provide new ways of interacting with audiovisual collections [148].