TITLE: COSMETIC APPLICATION OF AI & MACHINE LEARNING.
AUTHOR(S): Tanuja S.Chougule* ,Rutuja S. Chogule, Pradnya B. Ghatage, Diksha S. Kurane, Tejashree S. Khamkar
Embedded Files
Abstract:
The beauty and cosmeceutical industries have been greatly impacted by the quick development of artificial intelligence (AI) and machine learning (ML), which has resulted in the creation of novel, individualised, and scientifically based products. Researchers and formulators may now examine vast databases pertaining to consumer preferences, ingredient performance, and skin physiology thanks to AI and ML technologies, producing more individualised and efficient skincare products. These resources help with formulation optimisation, product stability forecasting, and evaluating any adverse effects likeallergy or irritation. Additionally, by providing real-time, data-based recommendations, AI-powered skin analysis systems and virtual try-on applications have revolutionised the consumer experience. In the cosmetics industry, machine learning algorithms are also essential for digital marketing, regulatory compliance, and quality control. Notwithstanding the many advantages, there are drawbacks to combining AI and ML, such as concerns about data privacy, algorithm transparency, and expensive implementation costs. The purpose of this review is to provide an overview of the present uses, benefits, drawbacks, and potential future developments of AI and ML in cosmetic science, emphasising their expanding contribution to the integration of pharmaceutical research and contemporary cosmetic innovation.
Keywords:
Artificial Intelligence, Machine Learning, Cosmeceuticals, Personalized Skincare, Formulation Optimization, Skin Analysis, Virtual Try-On, Cosmetic Innovation
Introduction:
The conventional formulation methods used in the cosmetics business have given way to extremely complex, technologically advanced systems. Machine learning (ML) and artificial intelligence (AI) have become potent instruments in recent years that are revolutionising the wayProducts for cosmetics are created, assessed, and customised. These technologies make it possible to make data-driven choices in areas including skin analysis, formulation optimisation, ingredient selection, and consumer recommendation systems. AI describes computer programs that are able to carry out operations like pattern recognition, problem solving, and decision making that normally call for human intellect. Algorithms in machine learning, a branch of artificial intelligence, learn from data and gradually get better at what they do. These methods, when used in beauty science, enable researchers to examine vast datasets of skin types, consumer preferences, and formulation characteristics in order to produce individualised and successful solutions. The use of AI in the cosmetic and cosmetics industries has increased because to the growing need for safe formulas, customised skincare, and effective product creation. Every facet of cosmetic research and marketing is changing due to technology, from virtual try-on systems and AI-powered skin diagnostic tools to predictive modelling of formulation stability. Furthermore, new avenues for intelligent, adaptable cosmetic treatments are being opened by the integration of AI with nanotechnology and biotechnology. With a focus on formulation creation, dermatological analysis, quality control, and customised beauty care, this paper attempts to examine the uses, benefits, difficulties, and possibilities of AI and ML in the cosmetics industry. The study also emphasises how these technologies usher in a new era of intelligent cosmetics by bridging the gap between pharmaceutical research and cosmetic innovation.
References
, J., et al. (2023). Environmental Data-Driven AI Models for Personalized Skincare Recommendations. Computational Toxicology, 25, 100345.
21.Lee, S., et al. (2024). Genetic Approaches to Personalized Cosmetic Formulations. Regulatory Toxicology and Pharmacology, 145, 105331.
22.Rahman, A., et al. (2022). Ethical and Bias Challenges in AI-Powered Dermatology and Cosmetics. Nature Digital Medicine, 5(3), 421–430.
23.Reuters. (2024). South Korean Beauty Buffs Can Now Thank AI for the Perfect Foundation Shade. https://www.reuters.com/technology/artificial-intelligence/south-korean-beauty-buffs-can-now-thank-ai-perfect-foundation-shade-2024-07-11
24.Zhao, Y., et al. (2024). Artificial Intelligence in Cosmetic Formulation: Predictive Modeling for Safety and Efficacy. Cosmetics, 12(4), 157.
25.Allure. (2025). L’Oréal Uses AI for Real-Time Quality Control in Cosmetics Manufacturing. https://www.allure.com
26.Jung, H., et al. (2023). AI-Based Prediction Models for Cosmetic Product Quality and Stability. Journal of Cosmetic Science, 74(3), 221–232.
27.Kim, J., et al. (2023). Machine Vision-Based Quality Inspection for Cosmetic Manufacturing. Computational Toxicology, 25, 100345.
28.Kostal, J., et al. (2022). AI in Toxicology: QSAR Modeling for Safer Chemical Design. Toxicological Sciences, 189(1), 50–65.
29.Lee, S., et al. (2024). Multi-Omics and Machine Learning Integration for Safety Assessment of Cosmetic Ingredients. Regulatory Toxicology and Pharmacology, 145, 105331.
30.OECD. (2023). Guidance Document on the Use of Artificial Intelligence in Toxicity Testing. Organisation for Economic Co-operation and Development, Paris.
31.Rahman, A., et al. (2022). Ethical and Bias Challenges in AI-Powered Dermatology and Cosmetics. Nature Digital Medicine, 5(3), 421–430.
32.Wu, Q., et al. (2023). In Silico Prediction of Cosmetic Ingredient Safety Using ML Models. Computers in Biology and Medicine, 160, 106921.
33.Zang, Y., et al. (2023). QSAR-Based Modeling for Cosmetic Ingredient Toxicity Prediction. Cosmetics, 10(2), 45.
34.Zhao, Y., et al. (2024). Artificial Intelligence in Cosmetic Formulation and Quality Control. Cosmetics, 12(4), 157.
35.BeautyMatter. (2024). How AI and Data Are Redefining Personalized Beauty Experiences. https://beautymatter.com
36.Perfect Corp. (2023). AI-Powered Personalization in Beauty and Skincare Marketing. https://www.perfectcorp.com
37.Revieve. (2024). AI in Beauty: Real Case Studies on Engagement and Growth. https://www.revieve.com
38.ScienceDirect. (2024). Unlocking the Potential of AI: Enhancing Consumer Engagement in the Beauty Industry. https://www.sciencedirect.com
39.Unilever. (2024). AI-Powered Personalized Experiences in Beauty Brands.
https://www.unilever.com
40.Lee, S., et al. (2023). AI Applications in Skin Analysis and Cosmeceutical Development. Journal of Cosmetic Dermatology, 22(8), 2015–2026.
41.Rahman, A., et al. (2022). Ethical and Bias Challenges in AI-Powered Dermatology and Cosmetics. Nature Digital Medicine, 5(3), 421–430.
42.Tschandl, P., et al. (2024). Human–Computer Collaboration for Skin Disease Recognition. The Lancet Digital Health, 6(2), e97–e106.
43.Zhao, Y., et al. (2024). Artificial Intelligence in Cosmetic Formulation and Quality Control. Cosmetics, 12(4), 157.
44.Bini, F. A., et al. (2024). Artificial intelligence and ethics in aesthetic medicine: A review of privacy and governance concerns. Frontiers in Artificial Intelligence, PMC12159716.
45.Rundo, F., et al. (2021). Ethical and privacy issues in facial recognition and AI applications. Frontiers in Robotics and AI, PMC8320316.
46.Pattni, S., et al. (2024). Artificial Intelligence in Cosmetics: Opportunities, Ethics, and Regulation. Cosmetics, 12(4), 157. MDPI.
47.CosmeticsDesign (2024). How beauty companies can mitigate risks associated with AI technology use. CosmeticsDesign.com.
48.Daneshjou, R., et al. (2022). Skin tone representation and bias in dermatology AI models. arXiv preprint, arXiv:2203.08807.
49.Kaur, G., & Singh, R. (2023). AI in skincare: Challenges and bias in datasets. AI and Ethics, Springer. DOI.
50.McKinsey & Company. (2024). How beauty players can scale GenAI in 2025. McKinsey.com.
51.Kaur, J., & Singh, P. (2024). Artificial intelligence applications in the cosmetic industry.
Multidisciplinary Research Journal, PDF.
52.SkinInc. (2024). The Future of AI in Skincare. Retrieved from https://www.skininc.com
53.GCI Magazine. (2024). AI is Already Reshaping Beauty. Retrieved from
https://www.gcimagazine.com
54. Cosmetechs. (2024). AI in Cosmetics: Future of Beauty Innovation. Retrieved from
https://cosmetechs.com
55. Reelmind.ai. (2024). AI in Beauty and Cosmetics Market: Future Trends. Retrieved from https://reelmind.ai
56. The Verge. (2025). L’Oréal’s AI-driven Bioprinting and Smart Skincare Devices.
Retrieved from https://www.theverge.com
57. Vogue Business. (2024). AI Bots and 3D-Printed Brows: L’Oréal Ups Its TechInvestment. Retrieved from https://www.voguebusiness.com
58. MDPI. (2024). Artificial Intelligence in Cosmetics: Opportunities, Ethics, and
Regulation. Cosmetics, 12(4), 157. Retrieved from https://www.mdpi.com
59. USD Analytics. (2024). AI in Cosmetic Market Report 2024–2030. Retrieved from https://www.usdanalytics.com
60. MDPI. (2024). Artificial Intelligence in Cosmetics: Opportunities, Ethics, and
Regulation. Cosmetics, 12(4), 157. Retrieved from https://www.mdpi.com/2079-
9284/12/4/157
61.Wiley Online Library. (2024). Artificial Intelligence and Machine Learning in
Dermatology and Cosmetic Science: Challenges and Opportunities. Journal of
Cosmetic Dermatology, 23(3), 1235–1244. Retrieved from https://onlinelibrary.wiley.com/doi/full/10.1111/jocd.16640
Page updated
Google Sites
Report abuse