Role Of Artificial Intelligence In Preoperative Planning And Aesthetic Outcome Prediction In Plastic Surgery: A Systematic Review

Abstract

Background

Artificial intelligence (AI) is transforming plastic surgery by enhancing precision in preoperative planning, surgical simulation, and aesthetic outcome prediction. Deep learning and machine learning models, particularly convolutional neural networks (CNNs), enable high-fidelity analysis of complex imaging data, improving surgical decision-making. This systematic review evaluates the effectiveness of AI applications in preoperative planning and aesthetic outcome prediction across reconstructive, aesthetic, and craniofacial procedures.

Methods:
Following PRISMA 2020 guidelines, a comprehensive search of PubMed, Scopus, Web of Science, Embase, and Google Scholar (2019–2025) identified peer-reviewed studies applying AI in plastic surgery. Eligible studies included original cross-sectional, retrospective, prospective, or case-control designs reporting model performance metrics. Data were narratively synthesized due to methodological heterogeneity.

Results:
Twelve studies met inclusion criteria: breast reconstruction (n=4), orthognathic surgery (n=4), rhinoplasty (n=2), blepharoplasty (n=1), and facial rejuvenation (n=1). AI models achieved accuracies ranging from 81% to 97.68%. CNNs demonstrated 85% accuracy in rhinoplasty classification and submillimeter prediction errors (0.69–0.94 mm) in orthognathic surgery, surpassing conventional methods. Machine learning predicted breast implant volume with r = 0.9335, and scar assessment models achieved ROC-AUC values up to 0.931—comparable to expert dermatologists.

Conclusion:
AI offers robust, data-driven decision support for preoperative planning and aesthetic outcome prediction in plastic surgery. Deep learning and computer vision improve accuracy, efficiency, and patient satisfaction. Future research should prioritize prospective, multicenter validation and standardized reporting to ensure clinical translation while addressing algorithmic bias and ethical concerns.