The paper presents a new multimodal rapid tuning method for ultrasound diagnosis of thyroid nodules that combines ultrasound images and textual descriptions. This method uses an image encoder and a fast tuning framework to efficiently retrieve representations from both modalities without the need for costly full model tuning. The obtained multimodal features are subsequently processed using a multilayer perceptron (MLP), which improves the diagnosis of the etiology of thyroid nodules. The results of extensive experiments on public and private datasets showed a significant accuracy improvement of up to 40.62% over ResNet and 28.51% over AlexNet for single-modal methods. Compared to other multimodal models, this method outperformed by 23.12% in accuracy and 25.21% in F1 score. The method also outperformed all participating radiologists in accuracy, indicating its potential for expert-level decision support. This approach can significantly facilitate faster and more consistent thyroid nodule screening, especially in areas with limited health resources.