Artificial Intelligence in Facial Plastic Procedures

Clinical Snapshot

Key Findings

• P Value: Not applicable - no statistical analysis

• Effect Size: Not quantified - descriptive findings only

• Primary Outcome: AI applications demonstrate potential in facial feature detection, surgical outcome prediction, and patient engagement

• Nnt Or Sensitivity: ML models can detect facial features correlating with post-rhinoplasty attractiveness, but specific accuracy metrics not provided

• Confidence Interval: Not provided

Clinical Application

Early-stage technology requiring further validation and integration into clinical workflows Limited applicability without TGA approval for specific AI medical devices; would require integration with existing plastic surgery training programs and RACS guidelines Facial plastic surgery patients, particularly those undergoing rhinoplasty and facial reconstruction

Abstract

OBJECTIVES: We reviewed the prospects of Artificial Intelligence (AI) and its usage in facial plastic surgery. METHODS: Literature search was performed using PubMed, Medline, Google and Google Scholar search engines between 2000 and 2025 years. RESULTS: AI is poised to impact the threefold missions of patient care, education and research. It plays a role in aiding clinician decision-making, serving as virtual conversational agents for patient engagement, and forecasting surgical outcomes. Primary areas of AI exploration in the medical field include machine learning (ML), neural networks (NN) and natural language processing (NLP). ML models have demonstrated the ability to detect facial features such as larger nasofrontal and nasolabial angles, correlating with increased attractiveness post-cosmetic rhinoplasty. Efficient resource allocation can be achieved through ML models guiding the analysis of post-operative free flap viability in facial skin cancer reconstruction. During online patient consultations, AI virtual assistants (AIVA) utilising NLP comprehend human speech intent, responding through dialogue. Unsupervised ML in facial analysis software is currently employed for correctly diagnosing rare diseases by recognising dysmorphic craniofacial features in two-dimensional photographs. AI also finds application in assessing post-operative outcomes, as well as in surgical training and research. CONCLUSION: The use of AI in healthcare is on the rise, contributing to advancements in pre-operative assessments, predicting prognoses, managing patients and monitoring postoperative progress. Surgeons are encouraged to explore collaboration with data scientists to shape the evolution of AI, aiming to enhance surgical efficiency, improve patient outcomes and potentially alleviate the workload associated with repetitive administrative duties.

References

1. 1.Bayar Muluk, N. (2026). Artificial Intelligence in Facial Plastic Procedures. Clinical Otolaryngology. https://doi.org/10.1111/coa.70052