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Machine learning is a field of artificial intelligence (AI), that uses complex algorithms to predict outcomes based on training datasets. Using the term “AI” has become widespread in tech companies, including now in the consumer health and beauty sphere. Machine learning and AI are used interchangeably. Companies are now looking to employ new technologies to improve the efficacy of their personal care products and aid consumers to make more informed skin care decisions.
What we know:
The global AI market is expected to grow twenty fold over the next decade from 200bn USD to nearly two trillion USD by 2030. (Statistia, 2023)
Following this trend, global cosmetic AI is currently valued at 3.22B USD and expected to grow by 19.6% between 2023 and 2031 to reach 15.75B USD
80% of American Gen Zs and 68.3% of European Gen Z are willing to trust AI for skincare recommendations (InsightAce Analytic 2023; Statistia, 2022ab)
AI models are thought to be capable of predicting the sensitising potential of ingredients with an accuracy of 86%, sensitivity of 80%, and specificity of 90% using their physicochemical characteristics and previous data on animal and in vitro models, showing the potential of this technology to be used when developing formulations that remain true to life (Kalicińska et al., 2023)
The surge in ‘generative AI’ has been implemented in formulation development and is thought to predict the effects of ingredients and their interactions on the skin, to a reasonable degree, especially for particular skin concerns (Vatiwutipong et al., 2023)
AI could be used to make predictions of how skin might change over time and make suggestions accordingly, increasing consumer temporal awareness of their skin condition (Gilliland 2023)
Industry impact & potential:
Companies are turning to AI to provide detailed skin analysis for individuals to tailor their products specifically to their skin needs. Shiseido’s skincare advisor assesses the quality of an individual's skin in under two minutes drawing from their database of 30,000 images to provide specialised skincare recommendations. Haut.AI ’s Skin SaaS system evaluates an individual's face based on multiple skin metrics including acne condition, hydration, and wrinkling to recommend products based on current and future predictions of skin.
Our solution:
For AI to be used effectively in the microbiome world, we need to train machine learning algorithms on high quality datasets as a starting point. At Sequential, we have collected nearly 20,000 skin microbiome samples, and the effect of hundreds of formulations containing over 4,000 ingredients. Our next step is to leverage this database for novel biomarkers, and formulations for certain skin demographics. We also have an expert team in the microbiome and AI field, including our advisor Prof Eran Segal who is experienced with big microbiome datasets.
References:
InsightAce Analytic. (2023). 80% of American Gen Zs and 68.3% of European Gen Z are willing to trust AI for skincare recommendations. Retrieved from
Kalicińska J, Wiśniowska B, Polak S, Spiewak R. Artificial Intelligence That Predicts Sensitizing Potential of Cosmetic Ingredients with Accuracy Comparable to Animal and In Vitro Tests-How Does the Infotechnomics Compare to Other "Omics" in the Cosmetics Safety Assessment? Int J Mol Sci. 2023 Apr 6;24(7):6801. doi: 10.3390/ijms24076801. PMID: 37047774; PMCID: PMC10094956.
Statista. (2022a). 80% of American Gen Zs and 68.3% of European Gen Z are willing to trust AI for skincare recommendations. Retrieved from https://www.statista.com/statistics/1289772/gen-z-s-trust-in-ai-beauty-advisors-in-north-america/
Statista. (2023). The global AI market is expected to grow twenty fold over the next decade from 200bn USD to nearly two trillion USD by 2030. Retrieved from https://www.statista.com/outlook/tmo/artificial-intelligence/worldwide
Vatiwutipong, Pat & Vachmanus, Sirawich & Noraset, Thanapon & Tuarob, Suppawong. (2023). Artificial Intelligence in Cosmetic Dermatology: A Systematic Literature Review. IEEE Access. PP. 1-1. 10.1109/ACCESS.2023.3295001.
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