Artificial Intelligence Approaches for Skin Anti-aging and Skin Resilience Research

Georgievskaya, Anastasia; Danko, David; Baxter, Richard A.; Corstjens, Hugo; Tlyachev, Timur

doi:10.1007/978-3-031-35176-1_10

Cited by 2 publications

(3 citation statements)

References 146 publications

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“…This article primarily focuses on bias in ML technologies as an important part of AI. ML-based applications are integrated in all segments of the cosmetic skincare industry chain, from basic research to customized services [9,10]. These ML applications include systems designed to capture individual characteristics of skin and hair from images and instrumental devices or collect information from online questionnaires, possibly with geolocation, etc.…”

Section: Key Stages Of the Ai Algorithm Development In Cosmetic Skinc...mentioning

confidence: 99%

“…These ML applications include systems designed to capture individual characteristics of skin and hair from images and instrumental devices or collect information from online questionnaires, possibly with geolocation, etc. On the one hand, this complex digital-based approach leads to a meaningful, well-founded outcome, such as an accurate skin diagnosis or product recommendation [9,10]. On the other hand, it carries the risk of bias and discrimination [12,13,27,33].…”

Section: Key Stages Of the Ai Algorithm Development In Cosmetic Skinc...mentioning

confidence: 99%

“…Digital tools in the field of beauty technology and cosmetic skincare include state-of-the-art techniques for collecting data on the characteristics of skin and hair. These tools interpret complex, multidimensional datasets and provide recommendations for a product or treatment [9][10][11]. These advanced approaches contribute to a truly personalized customer journey and shopping experience.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

How artificial intelligence adopts human biases: the case of cosmetic skincare industry

Georgievskaya,

Tlyachev,

Danko

et al. 2023

AI Ethics

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The cosmetic skincare industry is a growing market that extends to different regions and customer groups. In addition to scientific advances and technological developments, state-of-the-art digital approaches, including machine learning and other artificial intelligence (AI)-based techniques, are being applied at different stages of the value chain. The objectives of these efforts include optimizing the supply chain, developing high-quality, effective and safe products and personalization at every step of the customer journey. However, the use of digital technologies comes with risks and undesirable effects. These include a lack of transparency and accountability, compromised fairness and a general deficiency in data governance, all of which are critical at every customer touchpoint. This dark side of digital transformation is recognized by both businesses and governments. In this paper, we explain the concept of bias leading to unfairness for beauty technology applications. Based on published data we identified potential sources of AI bias in the cosmetic skincare industry and/or beauty tech. They were classified by the stage of the AI lifecycle: biases related to target setting, to acquisition and annotation, to modeling, to validation and evaluation, and to deployment and monitoring. We aim to create awareness of such phenomena among readers, whether executives, managers, developers or potential end-users.

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Section: Key Stages Of the Ai Algorithm Development In Cosmetic Skinc...mentioning

confidence: 99%

Section: Key Stages Of the Ai Algorithm Development In Cosmetic Skinc...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

How artificial intelligence adopts human biases: the case of cosmetic skincare industry

Georgievskaya,

Tlyachev,

Danko

et al. 2023

AI Ethics

View full text Add to dashboard Cite

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State-of-the-Art in Skin Fluorescent Photography for Cosmetic and Skincare Research: From Molecular Spectra to AI Image Analysis

Chekanov,

Danko,

Tlyachev

et al. 2024

Life

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Autofluorescence is a remarkable property of human skin. It can be excited by UV and observed in the dark using special detection systems. The method of fluorescence photography (FP) is an effective non-invasive tool for skin assessment. It involves image capturing by a camera the emission of light quanta from fluorophore molecules in the skin. It serves as a useful tool for cosmetic and skincare research, especially for the detection of pathological skin states, like acne, psoriasis, etc. To the best of our knowledge, there is currently no comprehensive review that fully describes the application and physical principles of FP over the past five years. The current review covers various aspects of the skin FP method from its biophysical basis and the main fluorescent molecules of the skin to its potential applications and the principles of FP recording and analysis. We pay particular attention to recently reported works on the automatic analysis of FP based on artificial intelligence (AI). Thus, we argue that FP is a rapidly evolving technology with a wide range of potential applications. We propose potential directions of the development of this method, including new AI algorithms for the analysis and expanding the range of applications.

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Artificial Intelligence Approaches for Skin Anti-aging and Skin Resilience Research

Cited by 2 publications

References 146 publications

How artificial intelligence adopts human biases: the case of cosmetic skincare industry

How artificial intelligence adopts human biases: the case of cosmetic skincare industry

State-of-the-Art in Skin Fluorescent Photography for Cosmetic and Skincare Research: From Molecular Spectra to AI Image Analysis

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