Background: Local flaps are a cornerstone of reconstructive plastic surgery for oncological skin defects, ensuring functional recovery and aesthetic integration. Their selection, however, varies with surgeon experience. Generative artificial intelligence has emerged as a potential decision-support tool, although its clinical role remains uncertain. Methods: We evaluated three generative AI platforms (ChatGPT-5 by OpenAI, Grok by xAI, and Gemini by Google DeepMind) in their free-access versions available in September 2025. Ten preoperative photographs of suspected cutaneous neoplastic lesions from diverse facial and limb sites were submitted to each platform in a two-step task: concise description of site, size, and tissue involvement, followed by the single most suitable local flap for reconstruction. Outputs were compared with the unanimous consensus of experienced plastic surgeons. Results: Performance differed across models. ChatGPT-5 consistently described lesion size accurately and achieved complete concordance with surgeons in flap selection. Grok showed intermediate performance, tending to recognise tissue planes better than lesion size and proposing flaps that were often acceptable but not always the preferred choice. Gemini estimated size well, yet was inconsistent for anatomical site, tissue involvement, and flap recommendation. When partially correct answers were considered acceptable, differences narrowed but the overall ranking remained unchanged. Conclusion: Generative AI can support reconstructive reasoning from clinical images with variable reliability. In this series, ChatGPT-5 was the most dependable for local flap planning, suggesting a potential role in education and preliminary decision-making. Larger studies using standardised image acquisition and explicit uncertainty reporting are needed to confirm clinical applicability and safety.

Background: The management of hard-to-heal wounds poses a major clinical challenge due to heterogeneous etiology and significant global healthcare costs (estimated at USD 148.64 billion in 2022). Large Language Models (LLMs), such as ChatGPT and Gemini, are emerging as potential decision-support tools. This study aimed to rigorously assess the accuracy and reliability of ChatGPT and Gemini in the visual description and initial therapeutic management of complex wounds based solely on clinical images. Methods: Twenty clinical images of complex wounds from diverse etiologies were independently analyzed by ChatGPT (version dated 15 October 2025) and Gemini (version dated 15 October 2025). The models were queried using two standardized, concise prompts. The AI responses were compared against a clinical gold standard established by the unanimous consensus of an expert panel of three plastic surgeons. Results: Statistical analysis showed no significant difference in overall performance between the two models and the expert consensus. Gemini achieved a slightly higher percentage of perfect agreement in management recommendations (75.0% vs. 60.0% for ChatGPT). Both LLMs demonstrated high proficiency in identifying the etiology of vascular lesions and recognizing critical “red flags,” such as signs of ischemia requiring urgent vascular assessment. Noted divergences included Gemini’s greater suspicion of potential neoplastic etiology and the models’ shared error in suggesting Negative Pressure Wound Therapy (NPWT) in a case potentially contraindicated by severe infection. Conclusions: LLMs, particularly ChatGPT and Gemini, demonstrate significant potential as decision-support systems and educational tools in wound care, offering rapid diagnosis and standardized initial management, especially in non-specialist settings. Instances of divergence in systemic treatments or in atypical presentations highlight the limitations of relying on image-based reasoning alone. Ultimately, LLMs serve as powerful, scalable assets that, under professional supervision, can enhance diagnostic speed and improve care pathways.