<p>Non-surgical cosmetic procedures have experienced significant growth, driven by advancements in technology and changing consumer demographics. The global non-surgical aesthetic market is projected to reach $90.2 billion by 2030, with Asia, particularly China, being a major contributor. Artificial Intelligence (AI) is revolutionizing these procedures by enhancing patient engagement, clinical decision-making, and operational efficiency. AI applications span from personalized aesthetic assessments and treatment planning to real-time monitoring and post-procedural care. This review synthesizes recent advancements in AI integration across the non-surgical cosmetic workflow, addressing challenges such as data privacy, algorithmic bias, and the need for comprehensive physician training. By adopting a multi-stakeholder perspective, the review highlights AI’s potential to democratize access to personalized, safe aesthetic care while emphasizing the importance of ethical considerations and regulatory frameworks.</p><p><i>Level of Evidence IV</i> This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors <a href="http://www.springer.com/00266">www.springer.com/00266</a>.</p>

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Artificial Intelligence in Non-Surgical Cosmetic Procedures: A Multi-Stakeholder Revolution

  • Jiancheng Li,
  • Youyou Li,
  • Jianquan Yan,
  • Lijun Yan,
  • Qin Li

摘要

Non-surgical cosmetic procedures have experienced significant growth, driven by advancements in technology and changing consumer demographics. The global non-surgical aesthetic market is projected to reach $90.2 billion by 2030, with Asia, particularly China, being a major contributor. Artificial Intelligence (AI) is revolutionizing these procedures by enhancing patient engagement, clinical decision-making, and operational efficiency. AI applications span from personalized aesthetic assessments and treatment planning to real-time monitoring and post-procedural care. This review synthesizes recent advancements in AI integration across the non-surgical cosmetic workflow, addressing challenges such as data privacy, algorithmic bias, and the need for comprehensive physician training. By adopting a multi-stakeholder perspective, the review highlights AI’s potential to democratize access to personalized, safe aesthetic care while emphasizing the importance of ethical considerations and regulatory frameworks.

Level of Evidence IV This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.