Artificial Intelligence-Assisted Reconstruction Planning of Cheek Soft Tissue Defects: A Comparative Study Against Surgical Expert Consensus.

BACKGROUND: Reconstructive planning for cheek soft tissue defects requires expert assessment of defect size and location to preserve aesthetic harmony. Artificial intelligence (AI), particularly large language models (LLMs), has emerged as a potential decision-support tool in plastic and reconstructive surgery. This study evaluates the accuracy and reliability of ChatGPT-4o in proposing reconstructive approaches for post-excisional cheek defects. METHODS: A comparative observational image-based study was performed using 46 standardized oblique facial photographs of healthy volunteers, on which cheek soft tissue defects were manually delineated to simulate post-excisional tissue loss. Each annotated photograph was submitted to ChatGPT-4o alongside a standardized prompt incorporating patient-specific clinical data. Two experts independently rated AI-generated responses across five domains using a five-point Likert scale (1-5). Inter-rater agreement was assessed using Cohen's kappa and intraclass correlation coefficient (ICC). RESULTS: ChatGPT-4o achieved high ratings across all domains: clinical accuracy 4.88/5, safety 4.59/5, aesthetic result 3.96/5, first reconstruction selection 4.05/5, and relevance of alternatives 3.65/5, with an average of 4.22/5. Strong inter-rater agreement was observed between evaluators (ICC = 0.75, 95% CI 0.57-0.86, p < 0.001), with high consistency for safety (κ = 0.82) and first reconstruction selection (κ = 0.76). However, ChatGPT-4o failed to generate illustrative depictions of the proposed reconstructive approaches. CONCLUSION: ChatGPT-4o demonstrated strong concordance with expert evaluators and shows promise as a decision-support adjunct in cheek reconstructive planning. However, its inability to generate visual depictions of proposed flaps remains a notable limitation. Expert supervision remains indispensable, and prospective clinical validation is warranted before routine implementation. 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 .