Frank Konstantin, Day Doris, Few Julius, Chiranjiv Chhabra, Gold Michael, Sattler Sonja, Kerscher Martina, Knoedler Leonard, Filippo Alexandre, Rzany Berthold, Cotofana Sebastian, Fabi Sabrina, Fritz Klaus, Peng Peter, Wanitphakdeedecha Rungsima, Pooth Rainer, Huang Patrick
Department of Plastic, Hand and Reconstructive Surgery, University Hospital Regensburg, Regensburg, Germany.
New York University Langone Health Medical Centers, New York, New York, USA.
J Cosmet Dermatol. 2024 Dec;23(12):4110-4115. doi: 10.1111/jocd.16481. Epub 2024 Aug 1.
Aesthetic medicine has traditionally relied on clinical scales for the objective assessment of baseline appearance and treatment outcomes. However, the scales focus on limited aesthetic areas mostly and subjective interpretation inherent in these scales can lead to variability, which undermines standardization efforts.
The consensus meeting aimed to establish guidelines for AI application in aesthetic medicine.
In February 2024, the AI Consensus Group, comprising international experts in various specialties, convened to deliberate on AI in aesthetic medicine. The methodology included a pre-consensus survey and an iterative consensus process during the meeting.
AI's implementation in Aesthetic Medicine has achieved full consensus for enhancing patient assessment and consultation, ensuring standardized care. AI's role in preventing overcorrection is recognized, alongside the need for validated objective facial assessments. Emphasis is placed on comprehensive facial aesthetic evaluations using indices such as the Facial Aesthetic Index (FAI), Facial Youth Index (FYI), and Skin Quality Index (SQI). These evaluations are to be gender-specific and exclude makeup-covered skin at baseline. Age and gender, as well as patients' ancestral roots, are to be considered integral to the AI assessment process, underlining the move towards personalized, precise treatments.
The consensus meeting established that AI will significantly improve aesthetic medicine by standardizing patient assessments and consultations, with a strong endorsement for preventing overcorrection and advocating for validated, objective facial assessments. Utilizing indices such as the FAI, FYI, and SQI allows for gender-specific, age adjusted evaluations and insists on a makeup-free baseline for accuracy.
美容医学传统上依赖临床量表对基线外观和治疗效果进行客观评估。然而，这些量表大多只关注有限的美学领域，且量表中固有的主观解读可能导致变异性，这有损标准化工作。
本次共识会议旨在制定人工智能在美容医学中应用的指南。
2024年2月，由各专业领域的国际专家组成的人工智能共识小组召开会议，商讨人工智能在美容医学中的应用。方法包括会前共识调查和会议期间的迭代共识过程。
人工智能在美容医学中的应用已就增强患者评估和咨询、确保标准化护理达成完全共识。人们认识到人工智能在防止过度矫正方面的作用，同时也认识到需要经过验证的客观面部评估。重点强调使用面部美学指数（FAI）、面部年轻化指数（FYI）和皮肤质量指数（SQI）等指标进行全面的面部美学评估。这些评估应针对不同性别，且在基线时排除化妆覆盖的皮肤。年龄、性别以及患者的祖籍被视为人工智能评估过程不可或缺的因素，突出了朝着个性化、精准治疗发展的趋势。
共识会议确定，人工智能将通过标准化患者评估和咨询显著改善美容医学，强烈支持防止过度矫正并倡导经过验证的客观面部评估。使用FAI、FYI和SQI等指标可进行针对性别、年龄调整的评估，并坚持以无妆基线确保准确性。
