  Ultrasound is a widely used imaging modality in clinical practice due to its
low cost, portability, and safety. Current research in general AI for
healthcare focuses on large language models and general segmentation models,
with insufficient attention to solutions addressing both disease prediction and
tissue segmentation. In this study, we propose a novel universal framework for
ultrasound, namely UniUSNet, which is a promptable framework for ultrasound
image classification and segmentation. The universality of this model is
derived from its versatility across various aspects. It proficiently manages
any ultrasound nature, any anatomical position, any input type and excelling
not only in segmentation tasks but also in classification tasks. We introduce a
novel module that incorporates this information as a prompt and seamlessly
embedding it within the model's learning process. To train and validate our
proposed model, we curated a comprehensive ultrasound dataset from publicly
accessible sources, encompassing up to 7 distinct anatomical positions with
over 9.7K annotations. Experimental results demonstrate that our model achieves
performance comparable to state-of-the-art models, and surpasses both a model
trained on a single dataset and an ablated version of the network lacking
prompt guidance. Additionally, we conducted zero-shot and fine-tuning
experiments on new datasets, which proved that our model possesses strong
generalization capabilities and can be effectively adapted to new data at low
cost through its adapter module. We will continuously expand the dataset and
optimize the task specific prompting mechanism towards the universality in
medical ultrasound. Model weights, data processing workflows, and code will be
open source to the public (https://github.com/Zehui-Lin/UniUSNet).
