Generative artificial intelligence (AI) is rapidly transforming medical imaging by enabling capabilities such as data synthesis, image enhancement, modality translation, and spatiotemporal modeling. This review presents a comprehensive and forward-looking synthesis of recent advances in generative modeling including generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models, and emerging multimodal foundation architectures and evaluates their expanding roles across the clinical imaging continuum. We systematically examine how generative AI contributes to key stages of the imaging workflow, from acquisition and reconstruction to cross-modality synthesis, diagnostic support, and treatment planning. Emphasis is placed on both retrospective and prospective clinical scenarios, where generative models help address longstanding challenges such as data scarcity, standardization, and integration across modalities. To promote rigorous benchmarking and translational readiness, we propose a three-tiered evaluation framework encompassing pixel-level fidelity, feature-level realism, and task-level clinical relevance. We also identify critical obstacles to real-world deployment, including generalization under domain shift, hallucination risk, data privacy concerns, and regulatory hurdles. Finally, we explore the convergence of generative AI with large-scale foundation models, highlighting how this synergy may enable the next generation of scalable, reliable, and clinically integrated imaging systems. By charting technical progress and translational pathways, this review aims to guide future research and foster interdisciplinary collaboration at the intersection of AI, medicine, and biomedical engineering.

Clinical neuroscience studies are based on neuroimaging and psychiatric. These studies are very important for genetic data processing, cognitive evaluations, and follow-up. Brain imaging describes the colorful ways to either directly or laterally structure and function the neuroimaging system. Neuroradiologists do the interpretation of structural and functional imaging and clinical analysis of the Brain. Structural imaging deals with the nervous system structure and records the opinion of intracranial complaints of excrescence and injury.

A vascular necrosis (AVN) of the femoral head is a common yet poten(cid:173) tially serious disorder which can be detected in its very early stages with magnetic resonance imaging. We have developed multi-layer perceptron networks, trained with conjugate gradient optimization, which diagnose A VN from single magnetic resonance images of the femoral head with 100% accuracy on training data and 97% accuracy on test data.

To address the problem of medical image recognition, computer vision techniques like convolutional neural networks (CNN) are frequently used. Recently, 3D CNN-based models dominate the field of magnetic resonance image (MRI) analytics. Due to the high similarity between MRI data and videos, we conduct extensive empirical studies on video recognition techniques for MRI classification to answer the questions: (1) can we directly use video recognition models for MRI classification, (2) which model is more appropriate for MRI, (3) are the common tricks like data augmentation in video recognition still useful for MRI classification? Our work suggests that advanced video techniques benefit MRI classification. In this paper, four datasets of Alzheimer's and Parkinson's disease recognition are utilized in experiments, together with three alternative video recognition models and data augmentation techniques that are frequently applied to video tasks. In terms of efficiency, the results reveal that the video framework performs better than 3D-CNN models by 5% - 11% with 50% - 66% less trainable parameters. This report pushes forward the potential fusion of 3D medical imaging and video understanding research.

Abbreviated summaryMorita-Sherman et al. demonstrate how sophisticated research imaging can cross the divide to clinical practice. They show how quantitative MR

Summary: A new AI algorithm can predict the onset of Alzheimer's disease with an accuracy of over 99% by analyzing fMRI brain scans. Researchers from Kaunas University, Lithuania developed a deep learning-based method that can predict the possible onset of Alzheimer's disease from brain images with an accuracy of over 99 percent. The method was developed while analyzing functional MRI images obtained from 138 subjects and performed better in terms of accuracy, sensitivity, and specificity than previously developed methods. According to World Health Organisation, Alzheimer's disease is the most frequent cause of dementia, contributing to up to 70 percent of dementia cases. Worldwide, approximately 24 million people are affected, and this number is expected to double every 20 years.

High-resolution magnetic resonance images can provide fine-grained anatomical information, but acquiring such data requires a long scanning time. In this paper, a framework called the Fused Attentive Generative Adversarial Networks(FA-GAN) is proposed to generate the super-resolution MR image from low-resolution magnetic resonance images, which can reduce the scanning time effectively but with high resolution MR images. In the framework of the FA-GAN, the local fusion feature block, consisting of different three-pass networks by using different convolution kernels, is proposed to extract image features at different scales. And the global feature fusion module, including the channel attention module, the self-attention module, and the fusion operation, is designed to enhance the important features of the MR image. Moreover, the spectral normalization process is introduced to make the discriminator network stable. 40 sets of 3D magnetic resonance images (each set of images contains 256 slices) are used to train the network, and 10 sets of images are used to test the proposed method. The experimental results show that the PSNR and SSIM values of the super-resolution magnetic resonance image generated by the proposed FA-GAN method are higher than the state-of-the-art reconstruction methods.

Multi-echo magnetic resonance (MR) images are acquired by changing the echo times (for T2 weighted) or relaxation times (for T1 weighted) of scans. The resulting (multi-echo) images are usually used for quantitative MR imaging. Acquiring MR images is a slow process and acquiring multi scans of the same cross section for multi-echo imaging is even slower. In order to accelerate the scan, compressed sensing (CS) based techniques have been advocating partial K-space (Fourier domain) scans; the resulting images are reconstructed via structured CS algorithms. In recent times, it has been shown that instead of using off-the-shelf CS, better results can be obtained by adaptive reconstruction algorithms based on structured dictionary learning. In this work, we show that the reconstruction results can be further improved by using structured deep dictionaries. Experimental results on real datasets show that by using our proposed technique the scan-time can be cut by half compared to the state-of-the-art.

The recent surveys, studies, forecasts and other quantitative assessments of the health and progress of AI provided new numbers regarding business leaders' assessment of China as a global AI leader, the current worldwide ranking of China's AI-related entrepreneurial and research activities, plans for AI adoption by U.S. enterprises and expectations regarding its impact on jobs, and the use of AI in face recognition, physical security monitoring, cashierless retail, categorizing open-ended survey responses, and detecting plant diseases and atrial fibrillation. A doctor examines a magnetic resonance image on a computer screen during the CHAIN Cup at the China National Convention Center in Beijing, June 30, 2018. A computer running artificial intelligence software defeated two teams of human doctors in accurately recognizing maladies in magnetic resonance images in a contest that was billed as the world's first competition in neuroimaging between AI and human experts. The U.S. Department of Homeland Security estimates face recognition will scrutinize 97% of outbound airline passengers by 2023 [The Economist] More than 4.5 million websites use reCAPTCHA and the system collects hundreds of millions of daily solves or more than 100 person-years of labor every day; Google/reCAPTCHA has extracted to date over $7 billion of free labor [hcaptcha] The Bureau of Labor Statistics' injury and illness database is built upon text-based descriptions of work-related injuries and illnesses it receives from workplaces across the country each year; categorizing the description into actionable data used to be done manually, but this year, the BLS has done 80% of that automatically using deep neural networks [governmentCIO] The AI market worldwide is estimated to grow by $75.54 billion from 2019 to 2023 [Technavio] The AI market worldwide is estimated to reach $202.57 Data is eating the world quote of the week: "The market for data labeling passed $500 million in 2018 and it will reach $1.2 billion by 2023, according to the research firm Cognilytica. This kind of work, the study showed, accounted for 80 percent of the time spent building A.I. technology"--The New York Times AI is "mimicking the brain" quote of the week: "Computer vision… is nothing like the human sort"--The Economist Robots are eating the world quote of the week: "A human can certainly move a part faster than a cobot [collaborative robot]. However, it does not take coffee breaks and continues to work for several hours after we have already gone home"--Pekka Myller, Ket-Met Robots are eating the world quote of the 19th century: "[A Linotype] could work like six men and do everything but drink, swear, and go out on strike"--Mark Twain

Arthritis is inflammation of the joints. With time, inflammation causes the cartilage of the joint to break down. Eventually, the cartilage wears away and bone is left rubbing on bone….ouch. Predicting the development of arthritis is a valuable tool that can be accomplished using MRI. These researchers evaluated the ability of a machine learning algorithm to classify in vivo magnetic resonance images of human articular cartilage for development of osteoarthritis.