Generative AI has significantly impacted healthcare, particularly in smart medical applications. Generative models, such as 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. Integrating generative models with IoT technology enables real-time data analysis and predictions, offering smarter healthcare services and supporting telemedicine. Challenges include computational demands, ethical concerns, and scenario-specific limitations. Generative models are pivotal in artificial intelligence, primarily designed to create novel data samples. They have vast potential in intelligent medical devices, aiding in efficient and intelligent product design and manufacturing. The concept of generative models emerged in the 1980s, with Geoffrey Hinton introducing the Deep Belief Network (DBN). As deep learning evolved, generative models became a crucial branch, with GANs introduced in 2014 by Ian Goodfellow and colleagues. GANs feature a generator and a discriminator, collaboratively producing realistic samples via adversarial training. In the medical field, generative models are used for processing unstructured data such as images, audio, and text. They are pivotal in medical image processing, text analysis, and image generation. These models facilitate tasks like segmentation, reconstruction, and enhancement of medical images, improving diagnostic accuracy. In medical text analysis, they support tasks like classification, clustering, and generation, enhancing comprehension of patient conditions and treatment plans. For medical image generation, they create virtual images and labels, supporting medical research and diagnosis. Generative models have extensive applications in computer vision, natural language processing (NLP), and the medical field. In computer vision, they excel in image generation, restoration, and editing. They generate new images by learning from vast datasets and facilitate tasks such as style transfer and image synthesis. In NLP, they are used for machine translation, text summarization, and dialogue systems, leveraging encoder-decoder structures and attention mechanisms. In healthcare, generative models significantly impact medical image processing, text analysis, and image generation. These models aid in segmentation, reconstruction, and enhancement of medical images, improving diagnostic accuracy. In medical text analysis, they classify, cluster, and generate medical texts, enhancing comprehension of patient conditions and treatment plans. For medical image generation, they create virtual images and labels, supporting medical research and diagnosis. The development of intelligent medical equipment has seen notable advancements, particularly with AI integration. Intelligent devices enhance diagnostic efficiency by analyzing vast amounts of medical data, providing early disease warnings and diagnoses. They also enable remote medical treatment and diagnosis through wireless communication technology, enhancing service convenience and efficiency. However, challenges such as data security remain, necessitating robust measures to protect sensitive patient information. Generative AI has transformed fields like computer vision, NLP, and creative arts. Notable milestones include GANs and VAEs, whichGenerative AI has significantly impacted healthcare, particularly in smart medical applications. Generative models, such as 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. Integrating generative models with IoT technology enables real-time data analysis and predictions, offering smarter healthcare services and supporting telemedicine. Challenges include computational demands, ethical concerns, and scenario-specific limitations. Generative models are pivotal in artificial intelligence, primarily designed to create novel data samples. They have vast potential in intelligent medical devices, aiding in efficient and intelligent product design and manufacturing. The concept of generative models emerged in the 1980s, with Geoffrey Hinton introducing the Deep Belief Network (DBN). As deep learning evolved, generative models became a crucial branch, with GANs introduced in 2014 by Ian Goodfellow and colleagues. GANs feature a generator and a discriminator, collaboratively producing realistic samples via adversarial training. In the medical field, generative models are used for processing unstructured data such as images, audio, and text. They are pivotal in medical image processing, text analysis, and image generation. These models facilitate tasks like segmentation, reconstruction, and enhancement of medical images, improving diagnostic accuracy. In medical text analysis, they support tasks like classification, clustering, and generation, enhancing comprehension of patient conditions and treatment plans. For medical image generation, they create virtual images and labels, supporting medical research and diagnosis. Generative models have extensive applications in computer vision, natural language processing (NLP), and the medical field. In computer vision, they excel in image generation, restoration, and editing. They generate new images by learning from vast datasets and facilitate tasks such as style transfer and image synthesis. In NLP, they are used for machine translation, text summarization, and dialogue systems, leveraging encoder-decoder structures and attention mechanisms. In healthcare, generative models significantly impact medical image processing, text analysis, and image generation. These models aid in segmentation, reconstruction, and enhancement of medical images, improving diagnostic accuracy. In medical text analysis, they classify, cluster, and generate medical texts, enhancing comprehension of patient conditions and treatment plans. For medical image generation, they create virtual images and labels, supporting medical research and diagnosis. The development of intelligent medical equipment has seen notable advancements, particularly with AI integration. Intelligent devices enhance diagnostic efficiency by analyzing vast amounts of medical data, providing early disease warnings and diagnoses. They also enable remote medical treatment and diagnosis through wireless communication technology, enhancing service convenience and efficiency. However, challenges such as data security remain, necessitating robust measures to protect sensitive patient information. Generative AI has transformed fields like computer vision, NLP, and creative arts. Notable milestones include GANs and VAEs, which