Artificial intelligence in plastic surgery: current applications, limitations, and ethical considerations

Abstract

Artificial intelligence (AI) is increasingly being applied in plastic surgery, which is a specialty that relies heavily on visual assessment, technical precision, and individualized clinical judgment. To summarize current applications of AI in plastic surgery and evaluate the associated methodological limitations and ethical considerations relevant to clinical practice and education. A narrative literature review was performed using PubMed, Scopus, and Web of Science from database inception through January 2026. Studies were included if they addressed AI applications in plastic surgery practice and education, as well as the ethical domain thereof. Evidence was synthesized across thematic areas, including image-based systems, large language models (LLMs), methodological constraints, and regulatory considerations. Published studies primarily describe image-based systems supporting facial analysis, surgical planning, and simulation. However, recent work has explored the use of LLMs for patient information, clinical support, and resident education. Reported performance varies depending on the dataset characteristics, study design, and clinical context. Common limitations include selection bias, small or non-diverse datasets, limited external validation, inconsistent assessment standards, and reduced model transparency. The ethical concerns include bias, informed consent, disclosure of AI involvement, and accountability in academic use, particularly in an industry where outcomes are subjective and closely linked to patient expectations. Current evidence supports the cautious use of AI as a supplementary tool rather than an independent decision-making system. Furthermore, broader clinical integration will require prospective validation, standardized reporting, and clear ethical and regulatory guidance.