Artificial Intelligence in Facelift Surgery: A Systematic Review of Objective Outcome Assessment and Patient-Perceived Rejuvenation
In brief
AI facelift ratings underestimate patients' perceived rejuvenation by 35% to 50%
Across nine studies, AI estimated postoperative age reductions of 1.7 to 5.8 years, but its ratings consistently fell 35% to 50% short of patients' perceived rejuvenation. AI also rated deeper muscle-supporting techniques more favorably than skin-only procedures, yet the evidence is limited and varied; these tools should complement, not replace, patient-reported outcomes.
- Journal
- Aesthetic plastic surgery (Q1)
- Published
- 28 October 2026
- Study design
- Systematic review of cohort studies
- Evidence level
- Level 2, Moderate (CEBM 2a)
- Authors
- Mehmet Ozdemir, Suat Morkuzu, Afshin Mosahebi
- PMID
- 42955309
- DOI
- 10.1007/s00266-026-06392-z
Why clinicians should know about it
- Picked for Plastic and Reconstructive Surgery (top studies of the week, 1 November 2026): Systematic review of facelift outcomes and patient-perceived rejuvenation
Abstract
INTRODUCTION: Artificial intelligence (AI) has emerged as a tool for objective outcome assessment in facelift surgery (rhytidectomy), yet the clinical validity and limitations of these technologies remain incompletely characterized. This review evaluates the evidence on AI applications in facelift surgery, focusing on neural network based age estimation, concordance with patient perception, and large language model (LLM) supported patient counseling. METHODS: A systematic review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Databases were searched for studies applying AI to facelift surgery with primary clinical data. RESULTS: Nine studies published between 2020 and 2025 met inclusion criteria. Convolutional neural networks quantified postoperative age reduction ranging from 1.7 to 5.8 years depending on surgical technique and follow-up interval. AI-derived estimates consistently underestimated patient-perceived rejuvenation by approximately 35% to 50%. Superficial musculoaponeurotic system-based techniques demonstrated greater AI-measured age reduction than skin-only procedures. LLMs achieved 85% to 88% diagnostic accuracy for patient counseling but exceeded recommended readability standards. CONCLUSIONS: AI enables objective quantification of facelift outcomes but demonstrates systematic discordance with patient perception. Current AI modalities should function as adjuncts to validated patient-reported outcome measures, not replacements. These preliminary findings warrant cautious interpretation given the limited and heterogeneous evidence base. Ethical concerns regarding algorithmic definitions of surgical success warrant consideration. LEVEL OF EVIDENCE III: 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 .
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.