The article "Rapid Review of Generative AI in Smart Medical Applications" by Yuan Sun and Jorge Ortiz explores the significant impact of generative models on healthcare, particularly in intelligent medical devices. Generative models, including Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), have revolutionized medical image generation, data analysis, and diagnosis. These models enhance diagnostic speed and accuracy, improve medical service quality, and reduce equipment costs. They are also integrated with IoT technology to facilitate real-time data analysis and predictions, supporting telemedicine and smart healthcare services. The article traces the evolution of generative models from the 1980s, highlighting key milestones such as the introduction of Deep Belief Networks (DBNs) by Geoffrey Hinton and the breakthrough of GANs by Ian Goodfellow in 2014. In the medical field, generative models are used for tasks like image segmentation, reconstruction, and enhancement, as well as text analysis and health monitoring. They aid in creating new medical images, generating virtual labels, and supporting disease diagnosis and treatment planning. The development of intelligent medical equipment has been significantly advanced by AI integration, enhancing diagnostic efficiency and enabling remote medical treatment. However, challenges such as data security remain, emphasizing the need for robust measures to protect patient information. Overall, generative models have transformed healthcare by improving medical image generation, data analysis, and diagnosis, providing essential tools for researchers and clinicians to enhance patient outcomes and drive innovation in medical research and treatment.The article "Rapid Review of Generative AI in Smart Medical Applications" by Yuan Sun and Jorge Ortiz explores the significant impact of generative models on healthcare, particularly in intelligent medical devices. Generative models, including Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), have revolutionized medical image generation, data analysis, and diagnosis. These models enhance diagnostic speed and accuracy, improve medical service quality, and reduce equipment costs. They are also integrated with IoT technology to facilitate real-time data analysis and predictions, supporting telemedicine and smart healthcare services. The article traces the evolution of generative models from the 1980s, highlighting key milestones such as the introduction of Deep Belief Networks (DBNs) by Geoffrey Hinton and the breakthrough of GANs by Ian Goodfellow in 2014. In the medical field, generative models are used for tasks like image segmentation, reconstruction, and enhancement, as well as text analysis and health monitoring. They aid in creating new medical images, generating virtual labels, and supporting disease diagnosis and treatment planning. The development of intelligent medical equipment has been significantly advanced by AI integration, enhancing diagnostic efficiency and enabling remote medical treatment. However, challenges such as data security remain, emphasizing the need for robust measures to protect patient information. Overall, generative models have transformed healthcare by improving medical image generation, data analysis, and diagnosis, providing essential tools for researchers and clinicians to enhance patient outcomes and drive innovation in medical research and treatment.