Audio description (AD) in cultural heritage settings serves as an accessibility service that enables blind and partially-sighted visitors to interact with the diverse visual content in museums and galleries. With the growing need to provide enabling services for a broader audience in audio-visual media, the implementation of artificial intelligence (AI) tools has become significant in cultural heritage contexts as well. In this context, the current study evaluates the applicability of Large Language Models (LLMs) and artificial intelligence (AI) tools through generating ADs of selected static artworks. It does so by implementing a corpus based linguistic analysis of AD texts that are produced by three modalities: (1) a Human-Authored AD Corpus; which consists of AD texts written by experts, (2) a Baseline AI AD Corpus which is a compilation of texts that an untrained AI model generates, and (3) a Trained AI AD Corpus in which AD texts are generated through an AI model that is specifically trained with domain-specific datasets. The results of the corpus analysis are presented and discussed within the framework of adherence to the characteristics of museum AD. As part of a vast doctoral research project, this study is the first attempt to investigate the linguistic attributes of Turkish museum AD and to train an AI model to generate AD texts for static artworks. The results of the analysis indicate that, while human-authored texts remain the gold standard for the time being, AI-based automatic description systems have significant potential to meet accessibility standards and facilitate the development of inclusive, semantically robust automatic description tools.