Skip to content
1981
image of Imperatives of using artificial intelligence in modern clothing design: Systematic review

Abstract

Over the past decade, artificial intelligence (AI) has rapidly permeated various human activities. AI developments are utilized by professionals in design-creative processes, including art, fashion, design and so forth. Consequently, the purpose of this study is to analyse the role of neural networks in contemporary fashion design and the operations of design, marketing and advertising companies. To achieve the set goal, the following methods were employed: analytical, comparative and methods of generalization and systematization. The research determined that the use of AI in modern fashion design offers potential for creating new opportunities and transforming traditional approaches to the creation and sale of fashionable products. The experience of using AI in design, marketing and advertising campaigns also evidences the successful application of neural networks in creating innovative collections, unconventional design solutions and in the development of personalized marketing and demand forecasting and future trend analysis. However, alongside the advantages, AI application also raises ethical and social issues. Concerns about the use of personal data, copyright and AI’s impact on employment in the fashion industry require expert attention and discussion. Furthermore, against the backdrop of the rapid development of AI, threats to humanity as a whole emerge, which also need consideration. The conclusions of this article may be useful for designers, art historians, marketers, AI specialists and anyone interested in the use of neural networks in contemporary fashion design and its influence on the fashion industry.

Loading

Article metrics loading...

/content/journals/10.1386/fspc_00341_1
2025-08-06
2026-04-15

Metrics

Loading full text...

Full text loading...

References

  1. Abbas, Waseem, Zhang, Zuping, Asim, Muhammad, Chen, Junhong and Ahmad, Sadique (2024), ‘AI-driven precision clothing classification: Revolutionizing online fashion retailing with hybrid two-objective learning’, Information, 15:4, https://doi.org/10.3390/info15040196.
    [Google Scholar]
  2. Biliakovych, Liana (2018a), ‘Conceptual models, principles and methods of forecasting in a modern costume design: Theoretical aspect’, Current Issues of History, Theory and Practice of Artistic Culture, 40, pp. 6879.
    [Google Scholar]
  3. Biliakovych, Liana (2018b), ‘Conceptual principles of fashion trends in design forecasting: Classical and postmodern discourses’, Bulletin of the Kharkiv State Academy of Design and Arts, 3, pp. 512.
    [Google Scholar]
  4. Braun, Virginia and Clarke, Victoria (2006), ‘Using thematic analysis in psychology’, Qualitative Research in Psychology, 3:2, pp. 77101, https://doi.org/10.1191/1478088706qp063oa.
    [Google Scholar]
  5. Buil, Roman, Piera, Miquel Angel and Ginters, Egils (2016), ‘Multi-agent system simulation for urban policy design: Open space land use change problem’, International Journal of Modeling, Simulation, and Scientific Computing, 7:2, https://doi.org/10.1142/s1793962316420022.
    [Google Scholar]
  6. Chakraborty, Samit, Hoque, Md. Saiful, Jeem, Naimur Rahman, Biswas, Manik Chandra, Bardhan, Deepayan and Lobaton, Edgar (2021), ‘Fashion recommendation systems, models and methods: A review’, Informatics, 8:3, https://doi.org/10.3390/informatics8030049.
    [Google Scholar]
  7. Chayka, Rostyslav and Zelenin, Vsevolod (2024), ‘Exploring the relationship between personality and subjective career success: A study of the big five traits among Ukrainian IT specialists’, Conhecimento & Diversidade, 16:41, pp. 34775.
    [Google Scholar]
  8. Choi, Woojin, Jang, Seyoon, Kim, Ha Youn, Lee, Yuri, Lee, Sang-Goo, Lee, Hanbit and Park, Sungchan (2023), ‘Developing an AI-based automated fashion design system: Reflecting the work process of fashion designers’, Fashion and Textiles, 10, article 39, https://doi.org/10.1186/s40691-023-00360-w.
    [Google Scholar]
  9. Dilmegani, Cem (2023), ‘Top 13 use cases/applications of AI in manufacturing in 2023’, AI Multiple, 10 February, https://research.aimultiple.com/manufacturing-ai/. Accessed 20 June 2024.
  10. Ding, Yujuan, Lai, Zhihui, Mok, Tracy and Chua, Tat-Seng (2023), ‘Computational technologies for fashion recommendation: A survey’, ACM Computing Surveys, 56:6, pp. 145.
    [Google Scholar]
  11. Ding, Yujuan, Ma, Yunshan, Wong, Wai Keung and Chua, Tat-Seng (2022), ‘Modeling instant user intent and content-level transition for sequential fashion recommendation’, IEEE Transactions on Multimedia, 24, pp. 2687700, https://doi.org/10.1109/tmm.2021.3088281.
    [Google Scholar]
  12. Fishman, Scott (2023), ‘How artificial intelligence is changing the fashion industry’, Immago, 28 February, https://immago.com/ai-fashion-industry/. Accessed 20 June 2024.
  13. Guan, Weilli, Song, Xuemeng, Zhang, Haoyu, Liu, Meng, Yeh, Chung-Hsing and Chang, Xiaojun (2022), ‘Bi-directional heterogeneous graph hashing towards efficient outfit recommendation’, in J. Magalhães, A. del Bimbo, S. Satoh and N. Sebe (eds), MM 22: Proceedings of the 30th ACM International Conference on Multimedia, Lisbon, Portugal, 10 October, New York: Association for Computing Machinery, pp. 26876.
    [Google Scholar]
  14. Han, Ahyoung, Wohn, Kwangyun and Ahn, Jaehong (2021), ‘Towards new fashion design education: Learning virtual prototyping using E-textiles’, International Journal of Technology and Design Education, 31:2, pp. 379400.
    [Google Scholar]
  15. Hardabkhadze, Iryna, Bereznenko, Sergey, Kyselova, Kateryna, Bilotska, Larysa and Vodzinska, Oksana (2023), ‘Fashion industry: Exploring the stages of digitalization, innovative potential and prospects of transformation into an environmentally sustainable ecosystem’, Eastern-European Journal of Enterprise Technologies, 1:13 (121), pp. 86101, https://doi.org/10.15587/1729-4061.2023.273630.
    [Google Scholar]
  16. Javaid, Shehmir (2023a), ‘5 AI training steps and best practices in 2023’, AI Multiple, 27 March, https://research.aimultiple.com/ai-training/. Accessed 20 June 2024.
  17. Javaid, Shehmir (2023b), ‘Top 4 AI use cases in fashion in 2023’, AI Multiple, 13 March, https://research.aimultiple.com/ai-in-fashion/. Accessed 21 June 2024.
  18. Kantor, Angelika and Kubiczek, Jakub (2021), ‘Polish culture in the face of the COVID-19 pandemic crisis’, Journal of Risk and Financial Management, 14:4, https://doi.org/10.3390/jrfm14040181.
    [Google Scholar]
  19. Karadayi-Usta, Saliha (2024), ‘Role of artificial intelligence and augmented reality in fashion industry from consumer perspective: Sustainability through waste and return mitigation’, Engineering Applications of Artificial Intelligence, 133:A, https://doi.org/10.1016/j.engappai.2024.108114.
    [Google Scholar]
  20. Kato, Natsumi, Osone, Hiroyuki, Sato, Daitetsu, Muramatsu, Naoya and Ochiai, Yoichi (2018), ‘DeepWear: A case study of collaborative design between human and artificial intelligence’, in Y. Fernaeus, D. McMillan and M. Jonsson (eds), TEI 18: Proceedings of the Twelfth International Conference on Tangible, Embedded, and Embodied Interaction, Stockholm, Sweden, 18 March, New York: Association for Computing Machinery, pp. 52936.
    [Google Scholar]
  21. Kherde, Ashwin, Jawade, Prashant, Mane-Deshmukh, Indrajeet and Mirajkar, Pradnyoday (2019), ‘Novel approach to fashion design using artificial intelligence’, International Journal of Science and Research, 8:5, pp. 28589.
    [Google Scholar]
  22. Kubiczek, Jakub, Hadasik, Bartłomiej, Krawczyńska, Dominika, Przedworska, Kornelia, Madarász, Erika Zsuzsanna and Ryczko, Aleksandra (2024), ‘Perspective of created value in consumer choice: Comparison of economic and ecological dimensions’, SAGE Open, 14:1, pp. 114, https://doi.org/10.1177/21582440241238516.
    [Google Scholar]
  23. Liao, Shuiying, Ding, Yujuan and Mok, Tracy (2023), ‘Recommendation of mix-and-match clothing by modeling indirect personal compatibility’, in I. Kompatsiaris (Yiannis), J. Luo, N. Sebe, A. Yao, V. Mezaris, S. Papadopoulos, A. Popescu and Z. Huang (Helen) (eds), ICMR 23: Proceedings of the 2023 ACM International Conference on Multimedia Retrieval, Thessaloniki, Greece, 12–15 June, New York: Association for Computing Machinery, pp. 56064.
    [Google Scholar]
  24. Lu, Zhi, Hu, Yang, Jiang, Yunchao, Chen, Yan and Zeng, Bing (2019), ‘Learning binary code for personalized fashion recommendation’, in K. M. Lee, K. Dana and M. Shah (eds), 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 15–20 June, Long Beach, CA: Institute of Electrical and Electronics Engineers, pp. 1055462.
    [Google Scholar]
  25. Lyndyuk, Andriy, Havrylyuk, Ivanna, Tomashevskii, Yurii, Khirivskyi, Roman and Kohut, Maryana (2024), ‘The impact of artificial intelligence on marketing communications: New business opportunities and challenges’, Economics of Development, 23:4, pp. 6071, https://doi.org/10.57111/econ/4.2024.60.
    [Google Scholar]
  26. Makhmudova, Gulzhamol, Baibolov, Kanat, Karatayev, Marat, Kumsbekov, Serik, Kajranbekov, Gabit and Serikuly, Zhandos (2019), ‘Analysis of the physico-mechanical properties of form-stable plush knitwear for outer clothing’, Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 379:1, pp. 16670.
    [Google Scholar]
  27. Mameli, Marco, Paolanti, Marina, Pietrini, Rocco, Pazzaglia, Giulia, Frontoni, Emanuele and Zingaretti, Primo (2022), ‘Deep learning approaches for fashion knowledge extraction from social media: A review’, IEEE Access, 10, pp. 154576, https://doi.org/10.1109/access.2021.3137893.
    [Google Scholar]
  28. Minh, Nhut Tran and Ngan, Ha Ngo (2021), ‘Digital fashion: An optimal solution for fashion industry during COVID-19 pandemic’, AIP Conference Proceedings, 2406, https://doi.org/10.1063/5.0066478.
    [Google Scholar]
  29. Mohammadi, Seyed Omid and Kalhor, Ahmad (2021), ‘Smart fashion: A review of AI applications in virtual try-on and fashion synthesis’, Journal of Artificial Intelligence and Capsule Networks, 3:4, pp. 284304, https://doi.org/10.36548/jaicn.2021.4.002.
    [Google Scholar]
  30. Mykhailova, Rada, Kolisnyk, Oleksandra, Beregovyi, Oleksandr, Vlasiuk, Vladyslav and Kurovska, Daria (2023), ‘Midjourney neural network as a tool for generating design graphics’, Art and Design, 1:21, pp. 10615.
    [Google Scholar]
  31. Naydenko, Yulia (2023), ‘Many believed: A photo of the Pope in a stylish down jacket stirred up the web’, New Voice, 28 March, https://life.nv.ua/ukr/krasota-i-moda/foto-papi-rimskogo-u-modnomu-puhoviku-rozburhalo-merezhu-podrobici-skandalu-foto-50313811.html. Accessed 23 June 2024.
  32. Оliinyk, Oksana (2023), ‘Creative industries in the epoch of artificial intelligence: Tendencies and challenges’, Culture and Contemporaneity, 25:2, pp. 39.
    [Google Scholar]
  33. Øverjordet, Pernille (2021), ‘Exploring digital design: Associations and emotions from nature’, in H. Grierson, E. Bohemia and L. Buck (eds), DS 110: Proceedings of the 23rd International Conference on Engineering and Product Design Education (E&PDE 2021), Herning, Denmark, 9–10 September, Oslo: OsloMet.
    [Google Scholar]
  34. Piera, Miquel Angel, Buil, Roman and Ginters, Egils (2016), ‘State space analysis for model plausibility validation in multi-agent system simulation of urban policies’, Journal of Simulation, 10:3, pp. 21626, https://doi.org/10.1057/jos.2014.42.
    [Google Scholar]
  35. Pingkasan, Chatwalee (2021), ‘How AI is changing the fashion industry’, IT Chronicles, 1 November, https://itchronicles.com/artificial-intelligence/how-ai-is-changing-the-fashion-industry/. Accessed 24 June 2024.
  36. Popay, Jennie, Roberts, Helen, Sowden, Amanda, Petticrew, Mark, Arai, Lisa, Rodgers, Mark, Britten, Nicky, Roen, Katrina and Duffy, Steven (2006), Guidance on the Conduct of Narrative Synthesis in Systematic Reviews: A Product from the ESRC Methods Programme, Lancaster: Lancaster University.
    [Google Scholar]
  37. Rusanov, Andriy (2023), ‘Elon Musk and over 1000 experts sign an open letter on the dangers of advanced AI: A moratorium on development and regulation is proposed’, IT Community, 29 March, https://itc.ua/ua/novini/ilon-mask-ta-ponad-1000-ekspertiv-pidpysaly-vidkrytyj-lyst-pro-nebezpeku-prosunutyh-shi-proponuyetsya-moratorij-na-rozrobku-ta-regulyuvannya/. Accessed 25 June 2024.
  38. Shete, Sakshi, Darshan, Ht, Thakare, Manish and Dhuri, Kanchan (2024), ‘AI based fashion stylist recommendation system’, in J. Rathee (ed.), Proceedings of the 2024 11th International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India, 28 February–1 March, New Delhi: Institute of Electrical and Electronics Engineers, pp. 697701.
    [Google Scholar]
  39. Subramani, Raja, Mustafa, Mohammed Ahmed, Ghadir, Ghadir Kamil, Al-Tmimi, Hayder Musaad, Alani, Zaid Khalid, Rusho, Maher Ali, Rajeswari, N., Haridas, D., Rajan, A. J. and Kumar, Avvaru Praveen (2024), ‘Advancements in 3D printing materials: A comparative analysis of performance and applications’, Applied Chemical Engineering, 7:2, https://doi.org/10.59429/ace.v7i2.3867.
    [Google Scholar]
  40. Veliev, Fazil, Mustafayeva, Esmira, Mamontov, Anatoliі, Shevtsov, Vadim, Zinchenko, Sergii and Rud, Anatoliy (2021), ‘Development of a procedure for determination of damage to seeds and cotton fibers in cotton cleaning machines’, EUREKA, Physics and Engineering, 2021:4, pp. 12533, https://doi.org/10.21303/2461-4262.2021.001944.
    [Google Scholar]
  41. Verma, Dhruv, Gulati, Kshitij and Shah, Rajiv Ratn (2020), ‘Addressing the cold-start problem in outfit recommendation using visual preference modelling’, in A. Chakraborty and De. Sen (eds), 2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM), New Delhi, India, 24–26 September, New Delhi: Institute of Electrical and Electronics Engineers, pp. 25156.
    [Google Scholar]
  42. Wang, Haosha and Rasheed, Khaled (2014), ‘Artificial intelligence in clothing fashion’, in H. Arabnia, David de la Fuente, E. Kozerenko, P. LaMonica, R. Liuzzi, J. Olivas and T. Waskiewicz (eds), Proceedings of the 2014 International Conference on Artificial Intelligence, Volume II, Las Vegas, NV, USA, 21–24 July, Las Vegas, NV: CSREA Press, pp. 48490.
    [Google Scholar]
  43. Yao, Wenxin, Uchida, Kaoru, Zou, Jianpeng and Zhong, Xiaolin (2022), ‘Image-based fashion recommendation with attention to users’ interests’, in Y. Xue, J. Chu and P. Shi (eds), VSIP 22: Proceedings of the 2022 4th International Conference on Video, Signal and Image Processing, Shanghai, China, 25–27 November, New York: Association for Computing Machinery, pp. 8186.
    [Google Scholar]
/content/journals/10.1386/fspc_00341_1
Loading
/content/journals/10.1386/fspc_00341_1
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a success
Invalid data
An error occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test