Burns from white phosphorus are rare and remain a challenge for clinicians. White phosphorus burns are often associated with smaller surface areas and high morbidity rates. Classed as a chemical burn, white phosphorus is used for military purposes and within industry, for the manufacture of fireworks and agricultural products. In this report, we discuss the case of a 40 years old female who sustained 2% Total Body Surface Area partial to full thickness burns from white phosphorus. The burns were treated conservatively with mafenide acetate on the medial calf and dorsum of foot and Flaminal Forte was used for the palmar region. The patient was discharged 22 days after admission and followed up in the outpatient clinic. Despite the use of pressure garments, hypertrophic scarring began to develop on the dorsum of her right foot. During peacetime, white phosphorus possess a significant danger to civilians. Awareness of the unique nature of white phosphorus among military burn clinicians should be emphasized.

Objective: This systematic review evaluates the current applications, advantages, and challenges of large language models (LLMs) in melanoma care. Methods: A systematic search was conducted in PubMed and Scopus databases for studies published up to 23 July 2024, focusing on the application of LLMs in melanoma. The review adhered to PRISMA guidelines, and the risk of bias was assessed using the modified QUADAS-2 tool. Results: Nine studies were included, categorized into subgroups: patient education, diagnosis, and clinical management. In patient education, LLMs demonstrated high accuracy, though readability often exceeded recommended levels. For diagnosis, multimodal LLMs like GPT-4V showed capabilities in distinguishing melanoma from benign lesions, but accuracy varied, influenced by factors such as image quality and integration of clinical context. Regarding management advice, ChatGPT provided more reliable recommendations compared to other LLMs, but all models lacked depth for individualized decision-making. Conclusions: LLMs, particularly multimodal models, show potential in improving melanoma care. However, current applications require further refinement and validation. Future studies should explore fine-tuning these models on large, diverse dermatological databases and incorporate expert knowledge to address limitations such as generalizability across different populations and skin types.