Vision transformer-based methods are advancing the field of medical
artificial intelligence and cancer imaging, including lung cancer applications.
Recently, many researchers have developed vision transformer-based AI methods
for lung cancer diagnosis and prognosis. This scoping review aims to identify
the recent developments on vision transformer-based AI methods for lung cancer
imaging applications. It provides key insights into how vision transformers
complemented the performance of AI and deep learning methods for lung cancer.
Furthermore, the review also identifies the datasets that contributed to
advancing the field. Of the 314 retrieved studies, this review included 34
studies published from 2020 to 2022. The most commonly addressed task in these
studies was the classification of lung cancer types, such as lung squamous cell
carcinoma versus lung adenocarcinoma, and identifying benign versus malignant
pulmonary nodules. Other applications included survival prediction of lung
cancer patients and segmentation of lungs. The studies lacked clear strategies
for clinical transformation. SWIN transformer was a popular choice of the
researchers; however, many other architectures were also reported where vision
transformer was combined with convolutional neural networks or UNet model. It
can be concluded that vision transformer-based models are increasingly in
popularity for developing AI methods for lung cancer applications. However,
their computational complexity and clinical relevance are important factors to
be considered for future research work. This review provides valuable insights
for researchers in the field of AI and healthcare to advance the
state-of-the-art in lung cancer diagnosis and prognosis. We provide an
interactive dashboard on lung-cancer.onrender.com/.

通过对基于视觉转换器的人工智能方法在肺癌成像应用方面的最新发展进行综述，本文提供了对视觉转换器如何提高肺癌的人工智能和深度学习方法的性能的关键洞察。此外，该综述还确定了推动该领域发展的数据集。视觉转换器是研究人员的首选之一，然而也报告了许多其他结构，其中将视觉转换器与卷积神经网络或 UNet 模型相结合。视觉转换器模型在发展肺癌应用的人工智能方法方面越来越受欢迎，但计算复杂性和临床相关性是未来研究的重要因素。该综述为人工智能和医疗保健领域的研究人员提供了宝贵的见解，以推进肺癌诊断和预后的最新技术。请访问 lung-cancer.onrender.com/ 获取交互式仪表盘。