  The development of effective machine learning methodologies for enhancing the
efficiency and accuracy of clinical systems is crucial. Despite significant
research efforts, managing a plethora of diversified clinical tasks and
adapting to emerging new tasks remain significant challenges. This paper
presents a novel paradigm that employs a pre-trained large language model as a
universal clinical multi-task decoder. This approach leverages the flexibility
and diversity of language expressions to handle task topic variations and
associated arguments. The introduction of a new task simply requires the
addition of a new instruction template. We validate this framework across
hundreds of tasks, demonstrating its robustness in facilitating multi-task
predictions, performing on par with traditional multi-task learning and
single-task learning approaches. Moreover, it shows exceptional adaptability to
new tasks, with impressive zero-shot performance in some instances and superior
data efficiency in few-shot scenarios. This novel approach offers a unified
solution to manage a wide array of new and emerging tasks in clinical
applications.
