Text-to-image model for prostaglandin-associated periorbitopathy counseling: a proof-of-concept study

To assess the feasibility and utility of a text-to-image artificial intelligence (AI) model in enhancing patient counseling on the cosmetic side effects of prostaglandin analogue (PGA) therapy. Cross-sectional study. Pre- and post-treatment periocular photographs of PGA-treated patients were collected. To simulate bilateral pre-treatment appearance, untreated eyes were mirrored. The Generative Fill feature powered by Adobe Firefly was applied to masked orbital regions, using descriptive text prompts to generate visualizations of prostaglandin-associated periorbitopathy (PAP), including upper eyelid ptosis, enophthalmos, and hypertrichosis. Prompts were iteratively refined to closely replicate known treatment-related changes. The AI model successfully produced visually realistic images within two minutes that closely resembled the actual post-treatment appearance of PAP. Key manifestations such as eyelash hypertrichosis, enophthalmos, deepened upper lid sulcus, and ptosis were effectively simulated using tailored prompts. This proof-of-concept study demonstrates that text-to-image AI may serve as a novel, rapid, and personalized tool for visualizing potential cosmetic side effects of PGA therapy. By enabling patients to preview changes on their own faces, this technology may enhance informed consent, set realistic expectations, and improve treatment adherence. Future research should evaluate patient perceptions, the accuracy of AI-generated outcomes, and integration into clinical workflows.