Applications and Development of Large Language Models in Dermatology

Dermatology diseases have multi-faceted and complicated clinical presentations, with the distribution of the available medical resources being skewed, which is very problematic in terms of diagnosis. The development of large visual models with convolutional neural networks and Transformer architectures alongside multimodal large models increases diagnostic challenges in dermatology with new methods of overcoming them due to the rapid growth of deep learning technologies. In this paper, attention is paid to four representative large-scale models, PanDerm, HOT-AI, MF Early Diagnosis, and SAMCL, and a systematic comparison is performed in relation to the dimensions, such as the scale of data, situations where they are applicable, the benefits, and limitations of the models. The analysis shows that both types of models focus on the dissimilarity of generalizability and specialization. To show promising results in few-shot learning, multimodal fusion, and robustness, however, such models usually lack representativeness within data, lack generalization, and adequate clinical validation. The theoretical context of model choice and implementation in practice, by assessing model performance and clinical adaptability, will identically indicate the direction to which technical optimization, clinical integration, and standardization methods should be taken in the future, which has a lot of implications in developing dermatological diagnosis to precision, efficiency, and inclusiveness.