Clinical Image Correction for Extraocular and Periocular Pathologies and Appearance-related Findings Via an AI-based Image-editing Tool

Abstract

Objective: To evaluate the pathology-specific correction performance and photorealistic output quality of the Gemini 2.5 Flash Image model (also known as "Nano Banana"; Google LLC, Mountain View, CA, USA) across multiple periocular and extraocular pathologies and appearance-related findings. Methods: This retrospective study included 45 standardized clinical photographs representing nine periocular/extraocular pathologies and appearance-related findings. Each image underwent single-step, prompt-based editing using Gemini 2.5 Flash Image. Two independent raters scored images on pathology correction (Q1) and naturalness (Q2) using 5-point Likert scales. Ordinal statistics, Gwet's agreement coefficient (AC), and multilevel cumulative logistic models were applied. Results: Median scores were high for both Q1 and Q2 across raters. Images rated >= 4 ranged from 84-91% for Q1 and reached 100% for Q2. Interrater agreement was excellent (Q1 AC=0.9466; Q2 AC=0.9878). Highest correction performance was observed for blepharoptosis and exotropia, while lower eyelid blepharoplasty need and thyroid eye disease showed comparatively reduced correction. Conclusion: Prompt based large language models enabled editing provided clinically meaningful correction with high photorealism across most pathologies, suggesting a practical alternative to dataset dependent conventional image generation approaches.