Agenda for the talk:

• Why do we need image synthetic data generation
  Around one-quarter of rare disease patients experience a significant delay in receiving a proper diagnosis, ranging from 5 years to 30 years on average. In recent years, though we have had the help of AI Diagnosis Assistant, it still suffers from the scarcity of real-world data for rare diseases. Leveraging image synthetic data generation to create MRI and CT images will help researchers and doctors overcome the difficulty and augment small medical datasets in the world of foundation models.

• Traditional method of image generation
  The traditional approach to image generation for medical image analysis relies on AI models that are designed for specific tasks, where experts go through the rigorous of labeling images, performing data augmentation, etc. Even with the rigorous approach, there is still not much data (Images) readily available for deep learning training as a result of privacy concerns making medical image analysis suffer some drawbacks. However, with the advent of the GenAI model powered by Generative Adversarial Networks, commonly called GANs, can generate new images based on the examples and patterns learned from vast datasets.

• Application in other fields:
  1)Entertainment
  In the entertainment industry, AI image generators create realistic environments and characters for video games and movies. This saves time and resources that would be used to manually create these elements.
  2)Marketing and Advertising
  In marketing and advertising, AI-generated images quickly produce campaign visuals. For instance, instead of organizing a photo shoot for a new product, marketers can use AI to generate high-quality images that can be used in promotional materials.
  3)Medical Imaging
  In the medical field, AI image generators play a crucial role in improving the quality of diagnostic images. For example, AI can be used to generate clearer and more detailed images of tissues and organs, which helps in making more accurate diagnoses.

• Models Overview:
  In this part, we will summarize the existing research and compare GAN, VAE, and Diffusion models to non-technical people and what are the pros and cons for each of them.

• Demo/Implementation
  We will demonstrate image generation and diagnosis using a generative AI approach, which was not possible because of less amount of data in medical imaging.

• Risks associated and privacy issues
  Address FDA and other regulatory bodies’ concerns. And whether the diffusion model can capture the complexity and nuances of real-world patient experiences and whether it could lead to biased, incomplete, or misleading insights. The authenticity and quality of AI-generated images heavily depend on the datasets used to train the models. So there’s always a big chance of bias.

• Key Takeaways & Innovation
  1) Learn how the generation of synthetic medical images is revolutionizing rare disease diagnosis and improving the accuracy of medical assessments.
  2) Gain insights into the ethical and regulatory considerations of using AI-powered image generation in the medical field.
  3)Explore how innovative technology can address data scarcity and contribute to more efficient and effective disease diagnosis processes.