Generative Artificial Intelligence Industry Analysis Report
# Introduction
Generative Artificial Intelligence (AI) is an important branch in today's technology field, and its applications in various industries are steadily increasing, including Industrial Internet, medical diagnosis, e-commerce, and intelligent manufacturing, among others.
# Key Areas of Analysis
- Industrial Internet Applications: The application of Generative AI in the Industrial Internet of Things (IIoT) has become a research hotspot. It uses deep generative models (DGMs) for intelligent analysis of big data, for tasks like anomaly detection, trust boundary protection, network traffic prediction, and platform monitoring. However, it still faces challenges in industrial applications, such as weak discriminative capabilities, insufficient interpretability, and poor generalization, which require further research (De, Bermúdez-Edo, Xu, & Cai, 2022) (opens new window).
- Medical Diagnostic Field: AI has significant potential in the field of diagnostic radiology, particularly in medical image analysis, including image classification, lesion detection, and segmentation. Additionally, Generative Adversarial Networks (GANs) have shown excellence in generating high-resolution CT and MRI images (Moawad et al., 2022) (opens new window).
- Business Analytics: Data science and AI have transformed business operations and decision-making processes. Neural networks and recommendation systems are becoming increasingly popular in finance, marketing, and business management (Lakhan, 2022) (opens new window).
- Materials Informatics: In materials development, generative deep models are used to rapidly create new high-performance compounds, especially with limited experimental data. They propose Nested Variational Autoencoders (VAEs) to enhance the generation of high-performance compounds with limited data, reducing the need for extensive experiments and increasing development efficiency (Osakabe & Asahara, 2023) (opens new window).
- Medical AI Applications: AI applications in the medical field are a significant focus, particularly in dentistry, where convolutional neural networks are used to process raw images and detect various dental diseases. Challenges in this field include data complexity and privacy issues (Baig, Azam, & Mushtaq, 2022) (opens new window).
- Smart Industry: The application of AI technologies is changing business models, especially in the construction industry, where computational intelligence methods like machine learning and deep learning improve project efficiency and safety (Demertzis, Demertzis, & Iliadis, 2023) (opens new window).
- Healthcare Industry: The application of AI in the healthcare industry faces numerous challenges, including data generation, privacy protection, disease prediction, and classification. They propose a novel hybrid AI classifier for the diagnosis prediction of chronic diseases, enhancing accuracy and efficiency (Alattab et al., 2022) (opens new window).
- Ethical Challenges: The application of AI in healthcare requires consideration of ethical issues such as data privacy, algorithm biases, and patient rights (Sciarretta, Romano, Trebbi, & Greco, 2022) (opens new window).
# Applications
1. Deep Generative Models in Industrial Internet
In the Industrial Internet of Things (IIoT), deep generative models (DGMs) are being applied for intelligent data analysis. These models combine the flexibility of deep learning with the inferential capabilities of probabilistic modeling for tasks like anomaly detection, trust boundary protection, network traffic prediction, and platform monitoring. However, these models face challenges in industrial applications, such as weak discriminative capabilities, insufficient interpretability, and poor generalization, which require further research (De, Bermúdez-Edo, Xu, & Cai, 2022) (opens new window).
2. AI Applications in Diagnostic Radiology
AI is experiencing significant growth in diagnostic radiology, particularly in medical image analysis. Deep learning technologies, especially convolutional neural networks, have made breakthroughs in tasks such as image classification, lesion detection, and segmentation in medical imaging. Additionally, Generative Adversarial Networks (GANs) have been employed to generate high