Drawing the Surgical Blueprint: Evaluating ChatGPT Versus Gemini Across Diverse Plastic Aesthetic Procedures

Preoperative facial markings are critical to surgical precision and aesthetic outcomes in plastic surgery, yet remain operator-dependent and variably documented. Generative artificial intelligence (AI), particularly large multimodal models, offers potential for the automated illustration of surgical plans. This study compares the performances of ChatGPT-4o and Gemini Advanced in generating standardised preoperative markings for aesthetic facial procedures. Methods: Six text prompts describing common facial aesthetic surgeries were developed using established marking protocols. Each prompt was submitted once to ChatGPT-4o and Gemini Advanced, yielding twelve illustrations. Three board-certified plastic surgeons independently evaluated the images using a five-domain Likert scale assessing incision clarity, anatomical accuracy, template conformity, clinical usefulness, and overall graphic quality. A composite score out of 25 was calculated. Data were analysed using paired t-tests, and interrater reliability was assessed with intraclass correlation coefficients. Results: ChatGPT-4o significantly outperformed Gemini Advanced in composite scores (mean 18.0 ± 1.4 vs. 13.9 ± 1.6, p = 0.001, Cohen’s d = 1.69). Superior performance was noted across all domains, particularly in clarity (mean difference 0.83, p = 0.002) and graphic quality (mean difference 0.90, p = 0.001). Interrater reliability was good (ICC = 0.82). Discussion: ChatGPT-4o demonstrated higher fidelity in translating surgical prompts into anatomically appropriate, clinically useful illustrations. However, neither system achieved the precision required for clinical implementation without revision. These models may serve as adjuncts in education and preliminary planning. Future work should explore model fine-tuning, surgeon-guided generation, and performance in reconstructive procedures.