背景:美容医学传统上依靠临床量表来客观评估基线外观和治疗结果。然而,量表主要集中在有限的美学领域,这些量表固有的主观解释可能导致可变性,这破坏了标准化工作。
目的:共识会议旨在建立AI在美学医学中的应用指南。
方法:2024年2月,人工智能共识小组,由各种专业的国际专家组成,召开会议审议美学医学中的人工智能。该方法包括共识前调查和会议期间的迭代共识过程。
结果:AI在美学医学中的实施在加强患者评估和咨询方面取得了充分共识,确保标准化护理。人工智能在防止过度校正方面的作用得到了认可,同时需要经过验证的客观面部评估。重点是使用面部美学指数(FAI)等指标进行全面的面部美学评价,面部青年指数(FYI),和皮肤质量指数(SQI)。这些评价是性别特异性的,并且在基线处排除化妆覆盖的皮肤。年龄和性别,以及病人的祖先根,被认为是人工智能评估过程不可或缺的部分,强调走向个性化,精确的治疗。
结论:共识会议确定,人工智能将通过规范患者评估和咨询来显著改善美学医学,强烈支持防止过度纠正和提倡验证,客观的面部评估。利用FAI等指数,仅供参考,SQI允许按性别区分,年龄调整后的评估,并坚持不化妆的基线以确保准确性。
BACKGROUND: 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.
OBJECTIVE: The consensus meeting aimed to establish guidelines for AI application in aesthetic medicine.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.