Noteworthy applications include the utilization of machine learning algorithms to identify and visualize color pigments in cultural heritage artworks (Chen et al. 2022). Through quantitative analysis, a growing research interest in the subject is evident, progressing from user perception approaches to the utilization of tools like deep learning for art studies. While AI has the potential to predict artistic trends, it is important to note that creativity and artistic expression are deeply personal and subjective. By continuously updating their models based on new data, these algorithms can adapt and refine their predictions over time. ResMem is a neural network developed to predict people’s memory for images in a lab setting. https://pixelsdesignagency.com/ Factors such as image size and placement in the gallery impacted memorability, and even though the tasks participants completed were different, the model, called ResMem, successfully predicted which images would be memorable in both cases.

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Deep Learning based approach for Photographs and Painting Classification using CNN Model Art history; Entropy; Information theory; Machine learning; Paintings; Wavelet transform Chinese painting; computer technology; computer vision technology; identification Deep Learning; Ensemble Methods; GRU based RNN; KNN; Machine Learning; Multi-label Emotion Classification; Naïve Bayes; One-way ANOVA; Python; Random Forest; SVM; Twitter Measuring the originality of intellectual property assets based on estimated inter-asset distances Hyperknowledge; Hyperlinked Knowledge Graph; Knowledge Graph Completion; Multimodal data

Personalised advertising and content, advertising and content measurement, audience research and services development

The anime was produced and animated with AI assistance during the process of cutting and conversion of photographs into anime illustrations and later retouched by art staff. The screensaver used AI to create an infinite animation by learning from its audience. In 1997, Sims created the interactive artificial evolution installation Galápagos for the NTT InterCommunication Center in Tokyo. In both 1991 and 1992, Sims won the Golden Nica award at Prix Ars Electronica for his videos using artificial evolution.

“We are actively working now on making systems that can identify which images are the best at diagnosing early dementia. The model is available for anyone to use (provided the work is non-profit), and researchers are exploring what makes different images memorable in various situations. It can then apply these mappings to new images it’s never seen before.” “It has artificial ‘neurons’ and it learns a mapping between an image and its memorability score through us showing it tens of thousands of examples.

• These results show a clear upward trend in interest and research in the use of machine learning techniques to address the prediction of artistic styles in paintings, highlighting the potential and relevance of this area in the scientific field.
• Synthography is a proposed term for the practice of generating images that are similar to photographs using AI.
• Art authentication; Box-counting algorithm; Fractal image analysis; Jackson Pollock; Painting analysis; Pattern recognition
• Cartoon Wall Art

Another related interesting work was by Belhi et al. (2018), addressing the challenge of automatically classifying and annotating cultural heritage artifacts using their visual characteristics and available metadata. The authors reexamined some recently successful experiments, showing that variations in image clarity in experimental datasets correlated with authenticity and might have acted as a confounding factor, artificially improving the results (Polatkan et al. 2009). While existing approaches exist, many tend to focus on specific methodologies or the classification of fragments rather than the comprehensive prediction of the artistic style of an entire work, as noted by Cascone et al. (2023). Additionally, studies have delved into the application of artificial intelligence for safeguarding Dunhuang cultural heritage, involving the creation of a comprehensive dataset in this domain (Yu et al. 2022). By embracing machine learning techniques, these models effectively surmount the constraints of traditional methods, yielding results that are both more precise and reliable (Mao 2022; Wenjing and Cai 2023).

The Implications for Artists

I’ve written before about how much textile art was present in art fairs, exhibitions, auctions and art fairs during 2024. This could create debates about the balance between creative intuition and data-driven decisions. AI tools will continue to disrupt traditional art-making processes.

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Artists can tweak settings like guidance scale (which balances creativity and accuracy), seed (to control randomness), and upscalers (to enhance image resolution), among others. When text-to-video is used, AI creates videos directly from text prompts, producing animations, realistic scenes, or abstract visuals. Flux.2 debuted in November 2025 with improved image reference, typography, and prompt understanding. In May 2025, Flux.1 Kontext by Black Forest Labs emerged as an efficient model for high-fidelity image generation, while Google’s Imagen 4 was released with improved photorealism.

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Additionally, it outlines study limitations and offers recommendations to enhance research validity. Similarly, emerging terms such as “Art Design”, “Supervised Machine Learning”, “Art History”, and “Image Generation” were observed, highlighting the evolution and expansion of the field into new areas of research and applications. With regard to the keywords that are currently trending, a solid domain of “Artificial Intelligence” and “Deep Learning” has been identified as consolidated concepts and protagonists in the field. The analysis of the thematic evolution revealed a transition in the orientation of the studies from a specialization in “Perceptive User Interfaces” to a greater focus on topics of “Artificial Intelligence” and “Deep Learning”. On the other hand, the most prominent countries in scientific production are the United States and China, reflecting their strong commitment and leadership in research related to this topic. Likewise, it was found that the journals “Conference On Intelligent User Interfaces” and “IEEE Journal on Select Topics in Signal Processing” are leaders in the publication of research on this topic, giving them a fundamental role in the dissemination of knowledge in the field.

Use of the term “art”

What issues are positioned as protagonists for the design of a research agenda on the use of machine learning for predicting artistic style? In this sense, the aim of this research is to examine the research trends in the use of machine learning for artistic style prediction, with the aim of providing a research agenda for future research. But while this growing understanding about what makes paintings memorable adds a layer of intrigue to how we experience images, memorability is just one facet of art appreciation. It could even predict which paintings were most famous by analyzing images with no additional historical or cultural context.

In this sense, a keyword co-occurrence analysis was carried out to identify the main thematic clusters related to the use of machine learning models to predict artistic styles in paintings (see Fig. 7). The concept of “perceptive user interfaces” played a fundamental role in the early years of research in the field of using machine learning models to predict artistic styles in paintings (see Fig. 6). In the field of deep learning models for predicting artistic styles in paintings, several outstanding journals have been identified in terms of impact and productivity (see Fig. 4). Similarly, by 2022, there are other relevant contributions in the field of using machine learning models to predict artistic styles in paintings. During the years 2020, 2021, and 2022, there was a significant surge in scientific production concerning deep learning models for predicting artistic styles in paintings (see Fig. 2). This analytical strategy provides a comprehensive view of the evolution and relevance of keywords in the field of deep learning models for predicting artistic styles in paintings.

Image captioning on fine art paintings via virtual paintings Classification of Chinese paintings; Deep learning; Embedded learning; Mutual information Big data; Machine learning; Master data; Production management; Shipbuilding; Statistical analysis Activity classification; Construction workers; Productivity analysis; Supervised machine learning; Wearable accelerometers Detection of forgery in paintings using supervised learning

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AI has revolutionized the ability to analyze and classify works of art, allowing the identification of stylistic characteristics with unprecedented accuracy. These gaps highlight areas and aspects that have not yet been fully explored or understood in the scientific literature. First, the exclusive focus on two databases may have limited the coverage of publications, excluding possible contributions from other relevant sources. The analysis of the thematic evolution shows a change in the research approach from “Perceptive User Interfaces” to aspects more related to “Artificial Intelligence”, “Deep Learning” and “Generative Adversarial Networks”.

• The present bibliometrics on the use of machine learning models to predict artistic styles in paintings, based on the PRISMA-2020 methodology and using the Scopus and Web of Science databases, provides a valuable perspective on the evolution and trends in the field.
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• These works were sometimes referred to as algorithmic art, computer art, digital art, or new media art.
• The increasing incorporation of artificial intelligence and machine learning in the artistic field can open up opportunities for artists and creators who want to experiment with innovative approaches and multidisciplinary collaborations.
• “It seems to me there is an increasing ‘Instagrammification’ of artwork and museums, and this sort of technology would be appealing to those applications.”
• This perspective encompasses a neutral stance, avoiding subjective interpretations or biases that could influence the study’s outcomes.

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The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. Evolution of entropy in art painting based on the wavelet transform Virtual restoration of paintings using adaptive adversarial neural network AI visual content creation; Contrastive learning; Text-to-image model Merging bioactivity predictions from cell morphology and chemical fingerprint models using similarity to training data Abstract ink painting; Generative art; Image generation; Machine learning

Additionally, articles in all languages are considered, with metadata uniformly translated into English within the databases. Additionally, the recent update to the PRISMA statement better aligns with the quantitative and qualitative criteria generated for obtaining results, enabling the combination of results from multiple databases and subsequent selection and cleaning of results for analysis. Finally, among the most recent works published in the last year, there is the contribution of Spee et al. (2023), who used Machine Learning to probe complex associations between 17 subjective artistic attributes and judgments of creativity in a wide range of artworks. Later, in a work by Tian and Nan (2022), published in 2022, they proposed a multitask convolutional neural network model for the emotion and rating of artworks.

Increased Role of Al in Art Production and Curation

Unlike previous algorithmic art that followed hand-coded rules, generative adversarial networks could learn a specific aesthetic by analyzing a dataset of example images. Classification of images; Crack detection; Supervised machine learning; Support vector machine CNN; conditional GAN; deep learning; image-to-image translation; watercolor art

For example, some work has focused on developing a framework for the restoration and conservation of visual cultural heritage using GAN techniques for the inpainting process. Of specific features in images (Vadicherla and Gadicha 2022). The other color clusters, Green, Yellow, Dark Blue, Orange, Lilac, and Pink, reflect different elements of conceptual affinity in this field of study.

Use of the term “art”

In the context of bibliometrics on the use of machine learning models to predict artistic styles in paintings, the inclusion criteria are based on three fundamental aspects. They posit that applying machine learning models to the analysis of artistic styles in paintings holds the promise of deepening our understanding of art history, the influence of masters on their students, and the evolution of trends over time. Machine learning models designed to forecast artistic styles in paintings leverage advanced algorithms capable of acquiring highly abstract and hierarchical representations from extensive datasets encompassing both historical and contemporary artistic images. In the field of art, machine learning models have been used to predict artistic styles in paintings. The study on using machine learning for predicting artistic styles in paintings offers a comprehensive overview of the field’s growth and evolution. This change allows us to conclude that there is an adaptation of researchers to current trends in research on the use of machine learning models to predict artistic styles in paintings, which contributes to the advancement and enrichment of knowledge in the discipline.

Philosophical context

Art; Color naming; Color similarity; https://hemerotecatarragonadigital.com/ Machine learning; Qualitative modelling; Support vector machines Adversarial examples; Autonomous driving; Bayesian optimization; End-to-end learning; Machine learning Africa; Food security; Machine learning; Malawi; Resilience; Shocks

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What is the thematic evolution derived from the scientific production on the use of machine learning for predicting artistic styles? This question arises with the aim of; Determining the primary and influential research works in the domain of machine learning used for predicting artistic styles, emphasizing key references that have significantly impacted this field. This question arises with the aim of; Understanding the growth pattern and trajectory of scientific articles focusing on the utilization of machine learning for predicting artistic styles over time. What is the growth in the number of scientific articles on the use of machine learning for predicting artistic styles?

The Limits of AI in Predicting Artistic Trends

Finally, those documents related to the field of health are excluded. First, the metadata of the title and abstract are considered as essential elements for the selection of the records. In order to achieve the objective of the research, a bibliometric analysis is proposed. A few of the articles included in this literature review, comprising the most cited articles from the 2000 to 2010 s and the most recent ones in the 2020s, are summarized below, as depicted in Table 1. They collected brushstrokes and manual brush movement samples from an artist, then trained a gen-erative model to generate brushstrokes belonging to the artist’s style. The researchers asked annotators to indicate the dominant emotion they felt for a specific image and, more importantly, to provide a well-founded verbal ex-planation for their emotion.