Taritsa Iulianna C, Foppiani Jose A, Escobar Maria Jose, Lee Daniela, Nguyen Khoa, Hernandez Alvarez Angelica, Schuster Kirsten A, Lee Bernard T, Lin Samuel J
Division of Plastic Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, 110 Francis Street Suite 5A, Boston, MA, 02215, USA.
Aesthetic Plast Surg. 2025 Jan 8. doi: 10.1007/s00266-024-04635-5.
Artificial intelligence (AI) technologies use a three-part strategy for facial visual enhancement: (1) Facial Detection, (2) Facial Landmark Detection, and (3) Filter Application (Chen in Arch Fac Plast Surg 21:361-367, 2019). In the context of the surgical patient population, open-source AI algorithms are capable of modifying or simulating images to present potential results of plastic surgery procedures. Our primary aim was to understand whether AI filter use may influence individuals' perceptions and expectations of post-surgical outcomes.
We utilized Amazon's Mechanical Turk platform and collected information on prior experience using AI-driven visual enhancement. The cohort was divided into two groups: AI-exposed and non-AI-exposed. Questions gauged confidence in plastic surgery's ability to meet participant expectations. A second survey exposed users to either AI-enhanced or to unenhanced pre-operative photographs. Then, unedited post-operative photographs were shown and surgery's ability to enhance appearance was assessed. A multivariable linear analysis was constructed to measure associations between exposure to AI enhancement and survey outcomes.
A total of 426 responses were analysed: 66.9% with AI exposure and 33.1% with no prior exposure. Participants with previous experience using AI-driven enhancers had a significantly higher average score for expectations after plastic surgery (P < 0.001). This finding was true across all outcomes, including surgery's ability to relieve discomfort with appearance/self-esteem (P < 0.001), to avoid post-operative complications (P < 0.001), to decrease post-operative scarring (P < 0.001), and to improve overall appearance (P < 0.001). The image comparison survey revealed that post-operative images were viewed as more successful at improving appearance when no pre-operative filter was applied (P = 0.151).
Exposure to AI photograph enhancement may significantly raise expectations for plastic surgery outcomes and may predispose to having lower satisfaction after surgery. The significance of this study lies in its potential to reveal the extent to which AI technologies can shape patient understanding of their plastic surgery outcomes. Plastic surgeons aware of the effect of AI enhancement may consider using these results to guide counselling.
his 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 .
人工智能(AI)技术采用三步策略进行面部视觉增强:(1)面部检测,(2)面部地标检测,以及(3)滤镜应用(Chen于2019年发表于《整形外科学档案》21:361 - 367)。在外科患者群体中,开源人工智能算法能够修改或模拟图像,以呈现整形手术的潜在结果。我们的主要目的是了解使用人工智能滤镜是否会影响个体对手术结果的认知和期望。
我们利用亚马逊的Mechanical Turk平台,收集了关于使用人工智能驱动的视觉增强的既往经验信息。该队列分为两组:接触人工智能组和未接触人工智能组。问题衡量了对整形手术满足参与者期望能力的信心。第二项调查让用户观看人工智能增强或未增强的术前照片。然后,展示未经编辑的术后照片,并评估手术改善外观的能力。构建了多变量线性分析,以测量接触人工智能增强与调查结果之间的关联。
共分析了426份回复:66.9%接触过人工智能,33.1%之前未接触过。有使用人工智能驱动增强器既往经验的参与者对整形手术后的期望平均得分显著更高(P < 0.001)。这一发现适用于所有结果,包括手术缓解外观/自尊不适的能力(P < 0.001)、避免术后并发症的能力(P < 0.001)、减少术后疤痕的能力(P < 0.001)以及改善整体外观的能力(P < 0.001)。图像比较调查显示,当未应用术前滤镜时,术后图像在改善外观方面被认为更成功(P = 0.151)。
接触人工智能照片增强可能会显著提高对整形手术结果的期望,并可能导致术后满意度降低。本研究的意义在于其揭示人工智能技术能够在多大程度上塑造患者对整形手术结果理解的潜力。意识到人工智能增强效果的整形外科医生可能会考虑利用这些结果来指导咨询。
证据级别III:本杂志要求作者为每篇文章指定证据级别。有关这些循证医学评级的完整描述,请参考目录或在线作者指南www.springer.com/00266 。