Data Sheet 1_Cosmetogenomics unveiled: a systematic review of AI, genomics, and the future of personalized skincare.docx

IntroductionThe integration of genomics, proteomics, and artificial intelligence (AI) is shaping the approach to personalized skincare and aesthetic dermatology, moving from generalized protocols toward precision-based interventions.
ObjectiveTo systematically review the emerging field of cosmetogenomics, focusing on how AI and multi-omics technologies are enabling personalized dermatologic treatments, and to critically evaluate the strength, scope, and limitations of current evidence.
MethodsWe conducted a systematic review in accordance with PRISMA 2020 guidelines. PubMed, Scopus, and Embase databases were searched for articles from January 2012 to April 2025 using Boolean combinations of terms including [“cosmetogenomics” OR “AI in dermatology” OR “personalized skincare” OR “multi-omics dermatology”] AND [“SNP” OR “genomics” OR “proteomics”]. Eligible studies included peer-reviewed clinical or ex vivo research involving human subjects and reporting measurable dermatologic outcomes related to genomics, single nucleotide polymorphisms (SNPs), AI tools, or proteomics. Study quality was assessed using the JAMA Users’ Guides to the Medical Literature quality scheme.
ResultsFrom 403 screened articles, 74 met inclusion criteria. Of these, 22 were randomized controlled trials (RCTs, Level I evidence), 35 observational studies (Level II), and 17 conceptual or expert opinion papers (Level III). AI and genomics were found to enhance skincare personalization by identifying SNPs associated with collagen degradation, oxidative stress, and inflammation. AI-powered platforms integrate these insights with imaging, lifestyle data, and digital twins to optimize interventions ranging from topical regimens to laser and injectable treatments. However, a significant proportion of studies were exploratory, with limited geographic diversity and underrepresentation of darker skin phototypes. No quantitative synthesis (meta-analysis) was performed due to heterogeneity in outcome measures, though hydration, elasticity, and pigmentation outcomes may permit such analysis in future work.
ConclusionAI-driven cosmetogenomics is advancing dermatology into a predictive, personalized era. While the evidence base is expanding, clinical translation requires stronger validation, ethical safeguards, and regulatory oversight. This field holds significant promise for enhancing treatment efficacy, patient satisfaction, and long-term skin health. Broader validation, greater diversity in study populations, more transparent methodologies, and expanded ethical safeguards, including genetic discrimination risks, data ownership, and cross-border data transfer, are necessary before widespread clinical integration.