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.

该论文介绍了一种利用预训练的大型语言模型作为通用临床多任务解码器的新范例，用于管理和适应多样化的临床任务及新任务的挑战，并验证了该框架在数百个任务上的鲁棒性，表现出与传统多任务学习和单任务学习方法相当的性能，对新任务具有出色的零样本表现和很强的数据效率，为临床应用中的各种新任务提供了统一的解决方案。

大型语言模型作为通用临床多任务解码器

Large Language Model as a Universal Clinical Multi-task Decoder

As computer vision algorithms are becoming more capable, their applications
in clinical systems will become more pervasive. These applications include
diagnostics such as colonoscopy and bronchoscopy, guiding biopsies and
minimally invasive interventions and surgery, automating instrument motion and
providing image guidance using pre-operative scans. Many of these applications
depend on the specific visual nature of medical scenes and require designing
and applying algorithms to perform in this environment.
In this review, we provide an update to the field of camera-based tracking
and scene mapping in surgery and diagnostics in medical computer vision. We
begin with describing our review process, which results in a final list of 515
papers that we cover. We then give a high-level summary of the state of the art
and provide relevant background for those who need tracking and mapping for
their clinical applications. We then review datasets provided in the field and
the clinical needs therein. Then, we delve in depth into the algorithmic side,
and summarize recent developments, which should be especially useful for
algorithm designers and to those looking to understand the capability of
off-the-shelf methods. We focus on algorithms for deformable environments while
also reviewing the essential building blocks in rigid tracking and mapping
since there is a large amount of crossover in methods. Finally, we discuss the
current state of the tracking and mapping methods along with needs for future
algorithms, needs for quantification, and the viability of clinical
applications in the field. We conclude that new methods need to be designed or
combined to support clinical applications in deformable environments, and more
focus needs to be put into collecting datasets for training and evaluation.

通过对 515 篇文献的审查，我们更新了医学计算机视觉领域中基于相机的手术和诊断中的跟踪和场景建模的状况，并总结了算法的最新发展，重点关注可变形环境下的算法，并讨论了对未来算法的需求、量化的需求以及临床应用的可行性。