408
J. Cosmet. Sci., 75.5, 408–422 (September/October 2024)
*Address all correspondence to Leslie Baumann, DrLeslieBaumann@skintypesolutions.com
AI-Guided Personalized Skin Care and Custom Routines
LESLIE BAUMANN
Former Professor of Dermatology, University of Miami, Miami, Florida, United States
Founder and CEO of Skin Type Solutions
Accepted for publication July 10, 2024.
Synopsis
Artificial intelligence (AI)-guided personalized skin care routine design requires accurate skin type diagnosis,
precise product labeling, and well-defined regimen templates. The Baumann Skin Typing System (BSTS),
a 16-skin type system used for decades by dermatologists, provides a solid foundation for AI-powered
customization of skin care. Utilizing the Baumann Skin Type® (MetaBeauty, Inc., Miami, FL) Indicator and
the Regimen Management System software—which contains over 40,000 regimen combinations and detailed
product tagging—professionals and consumers can create tailored routines that address multiple skin concerns
simultaneously. This comprehensive system, backed by decades of research and real-world application, utilizes
augmented intelligence to analyze large skin care datasets, identify patterns in user preferences, and provide
intelligent product recommendations that improve compliance and outcomes. The BSTS enhances the role
of cosmetic chemists by necessitating more sophisticated formulations that target multiple skin concerns,
making their expertise crucial. As AI models are trained with the BSTS knowledge base and dermatologist-
developed content and datasets, the integration of this system with AI technologies promises to revolutionize
skincare product development, marketing, and consumer experiences. This is ushering in a new era of precision
in personalized skincare products and will change the way the world shops for those products.
INTRODUCTION
Personalized skin care is a prominent trend in the cosmetics industry. With the rise of
artificial intelligence (AI), more companies offer personalized skin care routines. However,
most of these solutions fall short of being truly personalized and science-based, often
failing to deliver reproducible, effective outcomes that improve skin health. This is largely
due to inadequate diagnostic methods, insignificant data labeling, and haphazard product
categorization.1 This article discusses the importance of accurate skin phenotype diagnosis,
product selection, and routine design in achieving good outcomes. Proper regimen design
ensures that products enhance each other’s efficacy by placing synergistic products in the
correct step of a personalized skin care routine. The Baumann Skin Type®(BST)(MetaBeauty,
Inc., Miami, FL) Indicator (BSTI) and the associated Skin Type Solutions (STS)® (STS, Inc.,
Miami, FL) software both create science-based skin care regimens tailored to multiple skin
concerns. Augmented intelligence facilitates engagement, communication, education, and
motivation to improve consistency and compliance. Like a kaleidoscope creating complex
409 AI-Guided Skin Care
patterns from multiple elements, the STS algorithms synthesize numerous precisely
defined data points, generating a science-based, custom skin care regimen and personalized
educational content. This tailored approach culminates in quantifiable improvements in
skin health and reproducible positive outcomes.
IMPORTANCE OF CORRECT SKIN TYPE DIAGNOSIS
At the core of efficacious personalized skin care is accurate skin type diagnosis. In the realms
of machine learning and AI, the adage “garbage in, garbage out” holds true. Feeding an AI
system inaccurate skin type data will result in suboptimal product recommendations, lead
to incorrect advice, and possibly adversely affect skin health.2 With a live dermatologist
consult to diagnose skin type, other options to identify skin type include questionnaires,
imaging, and bioinstrumentation.
SKIN CARE QUIZ
The BSTI3,4 was developed and validated at the University of Miami between 2004 and
2010 using skin bioengineering devices such as the sebometer, tewameter, and mexameter.
This science-based skin type questionnaire is the only valid skin type test5 and is a
recognized diagnostic tool used in research studies to diagnose skin type (Figure 1).
Described in multiple medical textbooks6–11 and used by dermatologists12–14 and skin
care researchers worldwide,15–20 it is the gold standard in skin type diagnosis. The BSTI’s
accuracy is backed by various studies, confirming its reliability across different genders,
ethnicities, and geographic locations.21 This consistency is vital for researchers, cosmetic
chemists, aestheticians, medical providers, and dermatologists who rely on the BSTI for a
precise skin type diagnosis. Use of the BSTI quiz found at SkinTypeSolutions.com ensures
that the skin type data input into AI systems are accurate, enhancing the quality of
personalized skin care recommendations and educational content.
Figure 1. Author testing and perfecting the questionnaire in 2004 on dermatology patients at the University
of Miami Miller School of Medicine.
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Extracted Text (may have errors)

408
J. Cosmet. Sci., 75.5, 408–422 (September/October 2024)
*Address all correspondence to Leslie Baumann, DrLeslieBaumann@skintypesolutions.com
AI-Guided Personalized Skin Care and Custom Routines
LESLIE BAUMANN
Former Professor of Dermatology, University of Miami, Miami, Florida, United States
Founder and CEO of Skin Type Solutions
Accepted for publication July 10, 2024.
Synopsis
Artificial intelligence (AI)-guided personalized skin care routine design requires accurate skin type diagnosis,
precise product labeling, and well-defined regimen templates. The Baumann Skin Typing System (BSTS),
a 16-skin type system used for decades by dermatologists, provides a solid foundation for AI-powered
customization of skin care. Utilizing the Baumann Skin Type® (MetaBeauty, Inc., Miami, FL) Indicator and
the Regimen Management System software—which contains over 40,000 regimen combinations and detailed
product tagging—professionals and consumers can create tailored routines that address multiple skin concerns
simultaneously. This comprehensive system, backed by decades of research and real-world application, utilizes
augmented intelligence to analyze large skin care datasets, identify patterns in user preferences, and provide
intelligent product recommendations that improve compliance and outcomes. The BSTS enhances the role
of cosmetic chemists by necessitating more sophisticated formulations that target multiple skin concerns,
making their expertise crucial. As AI models are trained with the BSTS knowledge base and dermatologist-
developed content and datasets, the integration of this system with AI technologies promises to revolutionize
skincare product development, marketing, and consumer experiences. This is ushering in a new era of precision
in personalized skincare products and will change the way the world shops for those products.
INTRODUCTION
Personalized skin care is a prominent trend in the cosmetics industry. With the rise of
artificial intelligence (AI), more companies offer personalized skin care routines. However,
most of these solutions fall short of being truly personalized and science-based, often
failing to deliver reproducible, effective outcomes that improve skin health. This is largely
due to inadequate diagnostic methods, insignificant data labeling, and haphazard product
categorization.1 This article discusses the importance of accurate skin phenotype diagnosis,
product selection, and routine design in achieving good outcomes. Proper regimen design
ensures that products enhance each other’s efficacy by placing synergistic products in the
correct step of a personalized skin care routine. The Baumann Skin Type®(BST)(MetaBeauty,
Inc., Miami, FL) Indicator (BSTI) and the associated Skin Type Solutions (STS)® (STS, Inc.,
Miami, FL) software both create science-based skin care regimens tailored to multiple skin
concerns. Augmented intelligence facilitates engagement, communication, education, and
motivation to improve consistency and compliance. Like a kaleidoscope creating complex
409 AI-Guided Skin Care
patterns from multiple elements, the STS algorithms synthesize numerous precisely
defined data points, generating a science-based, custom skin care regimen and personalized
educational content. This tailored approach culminates in quantifiable improvements in
skin health and reproducible positive outcomes.
IMPORTANCE OF CORRECT SKIN TYPE DIAGNOSIS
At the core of efficacious personalized skin care is accurate skin type diagnosis. In the realms
of machine learning and AI, the adage “garbage in, garbage out” holds true. Feeding an AI
system inaccurate skin type data will result in suboptimal product recommendations, lead
to incorrect advice, and possibly adversely affect skin health.2 With a live dermatologist
consult to diagnose skin type, other options to identify skin type include questionnaires,
imaging, and bioinstrumentation.
SKIN CARE QUIZ
The BSTI3,4 was developed and validated at the University of Miami between 2004 and
2010 using skin bioengineering devices such as the sebometer, tewameter, and mexameter.
This science-based skin type questionnaire is the only valid skin type test5 and is a
recognized diagnostic tool used in research studies to diagnose skin type (Figure 1).
Described in multiple medical textbooks6–11 and used by dermatologists12–14 and skin
care researchers worldwide,15–20 it is the gold standard in skin type diagnosis. The BSTI’s
accuracy is backed by various studies, confirming its reliability across different genders,
ethnicities, and geographic locations.21 This consistency is vital for researchers, cosmetic
chemists, aestheticians, medical providers, and dermatologists who rely on the BSTI for a
precise skin type diagnosis. Use of the BSTI quiz found at SkinTypeSolutions.com ensures
that the skin type data input into AI systems are accurate, enhancing the quality of
personalized skin care recommendations and educational content.
Figure 1. Author testing and perfecting the questionnaire in 2004 on dermatology patients at the University
of Miami Miller School of Medicine.

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