  The advancement of artificial intelligence (AI) for organ segmentation and
tumor detection is propelled by the growing availability of computed tomography
(CT) datasets with detailed, per-voxel annotations. However, these AI models
often struggle with flexibility for partially annotated datasets and
extensibility for new classes due to limitations in the one-hot encoding,
architectural design, and learning scheme. To overcome these limitations, we
propose a universal, extensible framework enabling a single model, termed
Universal Model, to deal with multiple public datasets and adapt to new classes
(e.g., organs/tumors). Firstly, we introduce a novel language-driven parameter
generator that leverages language embeddings from large language models,
enriching semantic encoding compared with one-hot encoding. Secondly, the
conventional output layers are replaced with lightweight, class-specific heads,
allowing Universal Model to simultaneously segment 25 organs and six types of
tumors and ease the addition of new classes. We train our Universal Model on
3,410 CT volumes assembled from 14 publicly available datasets and then test it
on 6,173 CT volumes from four external datasets. Universal Model achieves first
place on six CT tasks in the Medical Segmentation Decathlon (MSD) public
leaderboard and leading performance on the Beyond The Cranial Vault (BTCV)
dataset. In summary, Universal Model exhibits remarkable computational
efficiency (6x faster than other dataset-specific models), demonstrates strong
generalization across different hospitals, transfers well to numerous
downstream tasks, and more importantly, facilitates the extensibility to new
classes while alleviating the catastrophic forgetting of previously learned
classes. Codes, models, and datasets are available at
https://github.com/ljwztc/CLIP-Driven-Universal-Model
