Applications of Generative AI

Zhihan Lv is Lecturer, Engineer and Researcher in Virtual Reality, Digital Twins and Metaverse, major in Mathematics and Computer Applied Technology. His research application fields widely range from everyday life to traditional research fields (i.e., geography and transportation, biology and chemistry, medicine and rehabilitation, industry and entertainment).  He has contributed 300 papers including more than 70 papers on IEEE/ACM Transactions. He is Editor-in-Chief of Internet of Things and Cyber-Physical Systems (KeAi) and Associate Editor of 22 journals including ACM Transactions on Multimedia Computing, Communications, and Applications, IEEE Transactions on Intelligent Transportation System, IEEE Transactions on Network and Service Management, IEEE Transaction on Computational Social Systems, IEEE Technology Policy and Ethics Newsletter, Early Career Advisory Board member of IEEE/CAA Journal of Automatica Sinica.

• 1. AI deep learning generative models for drug discovery.- 2. Generative AI in Medical Image Analysis.- 3. Transformer GAN for Fast MRI.- 4. Synthetic Data as a Tool to Combat Racial Bias in Medical AI: Utilizing Zero- Shot Text-to-Image Generative Models for Optimizing Skin Cancer Classifier Performance.- 5. Generative AI in Medical Imaging and its application in Low dose CT Image Denoising.- 6. Generative AI and drug discovery.- 7. Generative Adversarial Networks for medical imaging applications.- 8. Demoing laparoscopy surgery images using an GANs.- 9. Use of Generative Adversarial Network in Radiology.- 10. Generative Style Transfer in Histopathology.- 11. Generative adversarial networks s for Image Rendering.- 12. The ArtBench Dataset: Benchmarking Generative Models with Artworks.- 13. Generative Models for Culture-Aware Human-Robot Interaction.- 14. Generating Artistic Portrait Drawings from Images.- 15. Generative Models for Image Processing.- 16. Generative AI to understand complex ecological interactions.- 17. Generative AI for Architecture.- 18. Data augmentation of construction industry with Generative Adversarial Network.

“A timely and expansive guide to deploying generative AI across verticals with enough technical depth to serve as a serious reference for applied ML engineers. This book is particularly useful if you’re building domain-specific LLMs, integrating generative methods into existing enterprise workflows, or augmenting your AI stack with simulation and multi-modal reasoning.” (Raghav Sharma, Medium, medium.com, June 9, 2025)