Deep learning-enabled medical computer vision
By Andre Esteva, Katherine Chou, Serena Yeung, Nikhil Vijay Naik, Ali Madani, A. Mottaghi, Yun Liu, Eric J. Topol, Joseph Dean, Richard Socher. npj Digital Medicine, 2021
1,179 citations recorded by the source corpus.
A decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit—including cardiology, pathology, dermatology, ophthalmology–and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.
Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/c3df199cbca74763c4ae9889409bbd4aa29b6255
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Mohit Bansal
- Alan L. Yuille
- Quoc V. Le
- Jason Wei
- Hyung Won Chung
- Kyunghyun Cho
- Jianfeng Gao
- Yoav Goldberg
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Large language models encode clinical knowledge (2022, background): “, classification, regression, segmentation), lacking expressivity and interactive capabilities [21, 81, 97].”
- Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare (2022, background): “Neural Network (NN) • Cancer diagnosis [13,30–32] • Identifying Parkinson’s disease [33] • Image-based cardiac monitoring [22] • Alzheimer’s disease [34,35] • Surgery [25–27] • Sensor applications [36,37] • Diabetes prediction [38] • Human–machine interface [39] • Pandemic resource management [40] •”
- CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection (2023, background): “It is clinically important to make AI models faster [9,17].”