A Quantitative Framework for Image-Based Surgical Simulation Through Generative Architectures: A Pilot Application in Blepharoplasty and Facelift Procedures

Background: Facelift and blepharoplasty outcomes vary due to numerous factors. While patients once relied on before-and-after photos, they now increasingly look to AI-generated images for postoperative expectations. Due to the dataset training limitations of these AI models and the risk of unrealistic patient expectations, we sought to evaluate these AI models. Material and Methods: We utilized AI platforms DALLE, GetIMG, and Perchance to generate pre and post-operative images for facelift and blepharoplasty patients. A group of board-certified plastic surgeons and plastic surgery residents evaluated these images using 11 criteria, divided into categories of realism and clinical value. ANOVA and Tukey HSD Post-hoc statistical tests were used for data analysis. Results: Realism and clinical value showed no significant differences across AI models ( P = .21 and P = .59, respectively), yet GetIMG, (Mean ± Standard Deviation), (3.1 ± 1.12) significantly excelled in texture mapping against the others ( P < .01 and P = .03), and surpassed DALLE in age simulation accuracy ( P < .01). Across all models, healing and scarring prediction was the lowest performing metric (1.70 ± 0.42 P < .05). The evaluators also underscored the “uncanny valley” phenomenon. Conclusion: No significant differences were observed among the models’ realism or clinical value. Improving AI with real patient pre- and post-surgery photos is important to enhance accuracy and usefulness for surgeons and patients. Future research aims to compare AI-produced images with actual surgical photos and broaden the pool of expert evaluators.