Survey of Reshaping Generative Artificial Intelligence (AI) Research Landscape

Dec 27, 2023 ·

This article examines the potential application of artificial intelligence (AI) in the field of medical imaging. The authors focus on the development of deep learning algorithms to automate and improve diagnosis accuracy, making healthcare more cost-effective and accessible. They discuss various approaches to tackle the problems of medical image analysis, such as supervised and unsupervised learning, transfer learning, domain adaptation, and active learning.

The authors then examine the state-of-the-art AI methods that have been proposed for medical imaging. These include convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and autoencoders. They also survey recent applications, such as computer-aided diagnosis (CAD) systems, automatic segmentation, and 3D reconstruction.

Finally, the authors discuss challenges associated with the deployment of AI-based medical imaging systems. These include data privacy concerns, hardware limitations, and algorithmic robustness. They suggest a number of techniques to address these issues, such as federated learning, secure multi-party computation, and explainable AI.

Overall, this article provides an overview of the potential applications of AI in medical imaging. It discusses existing AI-based solutions, and highlights the challenges associated with deploying AI systems in the healthcare domain. It offers a number of strategies to address these challenges, helping to ensure the successful adoption of AI in medical imaging.