Multidisciplinary team (MDT) meetings are essential in melanoma management, ensuring collaborative decision-making among multiple specialist units. A key administrative task following these discussions is the generation of referral and recommendation letters, which communicate critical patient information to primary care providers and provide medicolegal documentation on the relevant medical record platform. Traditionally, this process is time-consuming. Artificial Intelligence (AI) can offer a promising solution by automating the drafting of these letters, enhancing efficiency, and ensuring high-quality communication. AI-driven Natural Language Processing models, trained on structured and unstructured medical data, can extract key clinical details from MDT discussions, including tumor staging, histology findings, treatment and surveillance recommendations. Machine learning algorithms refine letter composition, ensuring clarity, accuracy, and adherence to medical communication standards. AI was utilised to generate outcome letters for a melanoma MDT at a single centre in Victoria. This model of work reduces administrative workload, streamlines documentation and maintains medical accuracy. It has the benefit of ensuring standardised terminology and can minimize typing errors.