  The emerging trend of advancing generalist artificial intelligence, such as
GPTv4 and Gemini, has reshaped the landscape of research (academia and
industry) in machine learning and many other research areas. However,
domain-specific applications of such foundation models (e.g., in medicine)
remain untouched or often at their very early stages. It will require an
individual set of transfer learning and model adaptation techniques by further
expanding and injecting these models with domain knowledge and data. The
development of such technologies could be largely accelerated if the bundle of
data, algorithms, and pre-trained foundation models were gathered together and
open-sourced in an organized manner. In this work, we present OpenMEDLab, an
open-source platform for multi-modality foundation models. It encapsulates not
only solutions of pioneering attempts in prompting and fine-tuning large
language and vision models for frontline clinical and bioinformatic
applications but also building domain-specific foundation models with
large-scale multi-modal medical data. Importantly, it opens access to a group
of pre-trained foundation models for various medical image modalities, clinical
text, protein engineering, etc. Inspiring and competitive results are also
demonstrated for each collected approach and model in a variety of benchmarks
for downstream tasks. We welcome researchers in the field of medical artificial
intelligence to continuously contribute cutting-edge methods and models to
OpenMEDLab, which can be accessed via https://github.com/openmedlab.
