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<p>Aesthetic medicine is undergoing a&#xa0;substantial shift toward more objective, algorithm- and data-driven decision-making, with artificial intelligence (AI)-supported facial analysis playing a&#xa0;pivotal role. Modern systems such as CAARISMA™ capture the face holistically using anatomical landmarks, integrate these into the evaluation of different aesthetic dimensions, and translate them into easily interpretable scores such as Facial Aesthetic Index (FAI), Facial Youthfulness Index (FYI), and the Skin Quality Index (SQI). These indices enable standardized and reproducible aesthetic diagnostics, free from subjective, practitioner-specific influences. Clinically, they lead to more structured consultations, individualized treatment plans, and improved communication between physician and patient. At the same time, AI-based, data-driven aesthetic diagnostics and treatment planning can enhance efficiency and treatment quality in daily practice and allow for long-term monitoring of outcomes. Challenges at the interface between AI and aesthetics lie in the areas of bias, transparency, and data protection, all of which must be addressed to ensure responsible use of these technologies.</p>

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Künstliche Intelligenz-basierte holistische, individualisierte Gesichtsanalyse in der ästhetischen Medizin

  • 期刊论文

  • 发表日期：

    2026年1月14日

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  • Rainer Pooth

摘要

Aesthetic medicine is undergoing a substantial shift toward more objective, algorithm- and data-driven decision-making, with artificial intelligence (AI)-supported facial analysis playing a pivotal role. Modern systems such as CAARISMA™ capture the face holistically using anatomical landmarks, integrate these into the evaluation of different aesthetic dimensions, and translate them into easily interpretable scores such as Facial Aesthetic Index (FAI), Facial Youthfulness Index (FYI), and the Skin Quality Index (SQI). These indices enable standardized and reproducible aesthetic diagnostics, free from subjective, practitioner-specific influences. Clinically, they lead to more structured consultations, individualized treatment plans, and improved communication between physician and patient. At the same time, AI-based, data-driven aesthetic diagnostics and treatment planning can enhance efficiency and treatment quality in daily practice and allow for long-term monitoring of outcomes. Challenges at the interface between AI and aesthetics lie in the areas of bias, transparency, and data protection, all of which must be addressed to ensure responsible use of these technologies.