The rapid advancement of Generative AI impacted many areas in healthcare including chronic wound management. We conducted a systematic review to answer the following research question: How is generative AI used in the context of wound care and management? Our search across multiple databases resulted in more than 500 articles that matched our search criteria. After applying our inclusion/exclusion criteria, we identified 61 articles that are relevant to our research question. Preliminary analysis of these studies revealed four ways generative AI is utilized in chronic wound management context. First group of studies focus on generation of wound images from other images and utilize Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) to enhance wound image synthesis for training and diagnostics. This method aids in creating detailed visuals, improving early detection and effective wound management (Brüngel et al., 2023; Hreško et al., 2023). Second group of studies focus on generation of wound images from text to produce accurate wound images, improving communication among healthcare providers and supporting precise treatments. Key technologies like DALL-E2 generate high-resolution images, enhancing training and patient care (Brüngel et al., 2023). Third group of studies focus on generation of text from wound images and utilize methods such as CLIP (Contrastive Language Image Pretraining) that help analyze visual data to generate descriptive text, providing detailed documentation and deeper insights into wound characteristics, supporting clinical decisions. (Li et al., 2021). Forth group of studies focus on generation of text from text that utilize Large Language Models such as GPT-3 to enhancing insights from clinical notes and improve personalized treatment plans and diagnostics (Buchlak et al., 2022). In this talk, we will present the burgeoning applications of Generative AI in wound care and discuss the need for a balanced approach to address ethical and technological challenges.