August 31, 2023 - Generative artificial intelligence (AI) have shown great potential in enhancing medical imaging tasks such as data augmentation, image synthesis, image-to-image translation, and radiology report generation.

March 26, 2025 - Accurately interpreting medical images and generating insightful narrative reports is indispensable for patient care but places heavy burdens on clinical experts. Advances in artificial intelligence (AI), especially in an area that we refer to as multimodal generative medical image interpretation (GenMI), create opportunities to automate parts of this complex process.

April 1, 2024 - Our work shows how a generative model can be used to identify discrete medical imaging features associated with demographic information and systemic conditions. This approach allows researchers to improve their understanding of AI based models ...

September 6, 2023 - By Tanmai Sree Musalamadugu, Hemachandran Kannan. Generative AI plays a pivotal role in medical imaging analysis, enabling precise diagnosis, treatment planning and disease monitoring. Techniques like generative adversarial networks (GANs) and var…

August 14, 2025 - Generative artificial intelligence has emerged as a transformative force in medical imaging since 2022, enabling the creation of derivative synthetic datasets that closely resemble real-world data.

Overall, generative approaches ... their capacity to generate realistic data, drive innovation in image generation and manipulation, facilitate image-to-image translation, and open up creative opportunities for content generation across various domains....

The integration of generative AI models into medical imaging workflows is reshaping diagnostics, treatment planning, and research by addressing challenges such as data scarcity, image quality enhancement, and multimodal integration. Generative models may also provide opportunities for creating ...

November 1, 2024 - Image-based generative AI (hereafter referred to as image generative AI), including GAN and diffusion models, is commonly used for image quality enhancement, domain transfer, and imputation as augmented input data in the field of medical imaging.

July 27, 2023 - Recent advances in generative AI have brought incredible breakthroughs in several areas, including medical imaging. These generative models have tremendous potential not only to help safely share medical data via synthetic datasets but also to perform an array of diverse applications, such as anomaly detection, image-to-image translation, denoising, and MRI reconstruction.

Not only in pharmacology, but also in radiology, generative AI is changing the way people work by helping with the detection and segmentation of radiological image data or by improving image quality.

August 7, 2025 - Generative artificial intelligence (AI) is rapidly transforming medical imaging by enabling capabilities such as data synthesis, image enhancement, modality translation, and spatiotemporal modeling.

Although FMs require large datasets for initial training, they can be adapted to specific medical imaging tasks using smaller labeled datasets through techniques such as transfer learning, fine-tuning, prompt engineering, few-shot learning, and zero-shot learning, making them especially valuable in data-scarce settings. Many FMs also incorporate generative AI capabilities that support the creation of synthetic medical images to further address annotation limitations.

August 31, 2023 - Generative artificial intelligence (AI) have shown great potential in enhancing medical imaging tasks such as data augmentation, image synthesis, image-to-image translation, and radiology report generation.

With these large generative models, more precise and personal healthcare is possible. Generative AI can produce medical imaging data that gives a detailed look at human biology, enabling care specific to each patient and more personalized medicine.

GenAI provides medical professionals with additional visual cues through denoising, realistic imaging, and reconstruction techniques. They can use this information to analyse X-rays, MRIs, ultrasounds, CT scans, and DXAs.

Generative AI models, such as variational autoencoders (VAEs), generative adversarial networks (GANs), and text-to-image diffusion models, offer a promising solution to these challenges, as they can generate realistic and diverse images from ...

For example, a study in Nature Biomedical Engineering demonstrated that GAN-generated synthetic retinal images were just as effective as real images in training a deep learning model for diabetic retinopathy detection.1 · Despite the advancement with synthetic medical data, there are still limitations such as privacy and ethical considerations. To overcome these challenges, a 2024 study, MAISI (Medical AI for Synthetic Imaging), utilized diffusion models to generate high-resolution synthetic 3D CT images.

We introduce RadImageGAN, a new multi-modal (CT/MR/ultrasound) generative AI for radiology capable of generating 165 distinct classes with various patholog

September 27, 2023 - Generative AI in medical imaging analysis presents opportunities to automate and improve the accuracy of medical image analysis. Generation AI has found its use case in image segmentation, image synthesis, predicting patient outcomes, and detecting ...