Artificial Intelligence–Driven Life Cycle Assessment of Injectable Aesthetic Products: A Multi-Regional and Temporal Sustainability Analysis

Abstract

The environmental implications of injectable aesthetic products have not been thoroughly investigated, despite the rapid expansion of the aesthetic medicine sector. This study developed an innovative Artificial Intelligence (AI)-supported life cycle assessment (AI-LCA) framework, aligned with ISO 14040/14044 standards, to evaluate the carbon footprint of common products such as botulinum neurotoxins, hyaluronic acid fillers, and biostimulators. A comprehensive dataset was created covering four regions (European Union, United States, Asia-Pacific, and Türkiye) and five years from 2020 to 2024. The dataset was expanded from 300 to 9,960 records using Monte Carlo simulation, integration of regional environmental factors, and temporal expansion techniques. The model, trained using the Random Forest algorithm, demonstrated satisfactory predictive performance (R² = 0.954; root mean square error [RMSE] = 0.132; mean absolute error [MAE] = 0.075) and was compared with algorithms such as XGBoost and linear regression. Model stability was confirmed through 5-fold cross-validation, while generalizability was assessed using Leave-One-Region-Out Cross-Validation (mean R² = 0.769) and Temporal Hold-Out Validation (mean R² = 0.992). Bootstrap-derived 95% confidence intervals indicated narrow prediction uncertainty (relative uncertainty: 3.7–4.6%). SHapley Additive exPlanations (SHAP) analysis identified energy consumption and regional carbon intensity as the dominant predictors. Product type and cold chain requirements were found to be the main factors determining environmental impact; botulinum neurotoxins, which require refrigeration, exhibited an approximately 7.8-fold higher carbon footprint than non-refrigerated products. A reduction of approximately 8.5% in emissions was observed over five years, attributed to increased use of renewable energy, technological advances, and efficiency improvements in production processes. The AI-LCA framework provides a promising contribution to promoting sustainable aesthetic practices and increasing environmental awareness.

Keywords

• Life cycle assessment
• Artificial intelligence
• Aesthetic medicine
• Sustainability
• Carbon footprint
• Machine learning

Ethical Statement

Ethics committee approval was not required for this study because of there was no study on animals or humans.

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